# Multi-task Explainable Skin Lesion Classification

Mahapara Khurshid  Mayank Vatsa  Richa Singh

###### Abstract

Skin cancer is one of the deadliest diseases and has a high mortality rate if
left untreated. The diagnosis generally starts with visual screening and is
followed by a biopsy or histopathological examination. Early detection can aid
in lowering mortality rates. Visual screening can be limited by the experience
of the doctor. Due to the long tail distribution of dermatological datasets
and significant intra-variability between classes, automatic classification
utilizing computer-aided methods becomes challenging. In this work, we propose
a multitask few-shot-based approach for skin lesions that generalizes well
with few labelled data to address the small sample space challenge. The
proposed approach comprises a fusion of a segmentation network that acts as an
attention module and classification network. The output of the segmentation
network helps to focus on the most discriminatory features while making a
decision by the classification network. To further enhance the classification
performance, we have combined segmentation and classification loss in a
weighted manner. We have also included the visualization results that explain
the decisions made by the algorithm. Three dermatological datasets are used to
evaluate the proposed method thoroughly. We also conducted cross-database
experiments to ensure that the proposed approach is generalizable across
similar datasets. Experimental results demonstrate the efficacy of the
proposed work.

###### keywords:

Deep Learning, Few-shot Learning, Medical Imaging, Melanoma Classification,
Prototypical Networks, Segmentation

Indian Institute of Technology, Jodhpur, India

##  1 Introduction

Skin diseases is a common human illness that affects between 30 to 70% of
people and can worsen if left untreated. These diseases can be caused by a
number of factors, including microscopic bacteria, a fungus that thrives on
the skin, some kind of allergies or pigmentation [1]. These cause lesions on
the skin. The skin lesions can refer to any imperfection or defect located
either above or below the skin surface. These lesions have been divided into
two categories - benign and malignant. Benign lesions correspond to non-
threatening skin tumours such as cysts or moles; malignant tumours are
cancerous lesions, including melanoma, squamous cell carcinoma, and basal cell
carcinoma, among others [2]. Melanoma originates from the melanocyte cells
having a mole-like appearance and is often black or brown in colour [3].
Despite the fact that melanoma constitutes only 7% among others, it has a
mortality rate of 75%, making it the deadly type of skin cancer [4]. According
to WHO [5], there were around 324,000 ”melanoma of the skin” cases globally in
2020. Furthermore, roughly 106,110 additional cases and 7,180 skin cancer
deaths are estimated in the United States alone in 2021, according to [6].
Early detection of melanoma can prevent metastases and, as a result, mortality
in the majority of cases.

An experienced dermatologist diagnoses skin cancer visually, followed by
various clinical tests such as biopsy or histopathological examination.
Generally, the doctors use dermoscopy, a non-invasive procedure, that
magnifies the region at a high resolution and help them to clearly identify
the minute spots and other details of the lesions. However, it is limited by
human vision, perception and sensitivity. It requires significant training and
experience in addition to the time-consuming procedure. This triggered the
research community to develop precise automated approaches to assist doctors
in early screening. This research aims to propose a non-invasive computer-
aided screening method to help classify skin lesions and aid in the visual
screening process.

Classification of skin lesion images has a relatively extensive literature.
Some early methods use handcrafted features [7, 8, 9, 10] such as texture,
colour, and shape. However, these methods have limitations due to high visual
similarity, intra-class variability, and the presence of various artefacts.
Furthermore, these methods were dataset-specific and did not generalize well
across datasets. In addition to the boundary between the lesion and the
healthy skin, as shown in Fig. 1, artefacts such as hair, reference scales and
low contrast are examples that might affect classification performance. This
leads to the need to extract the region of interest (ROI) before performing
classification. It led to the development of approaches that are dependable,
resilient, and generalizable.

Figure 1: Showcasing the skin samples from HAM10000 (top row), PH2 (middle
row) and Derm7pt (bottom row). This figure highlights the need for
segmentation in skin lesion classification, as there are artifacts that need
to be removed before classifying the image.

In the field of medicine, deep learning has had a lot of success [11, 12, 13,
14, 15]. These approaches have shown good performance [16], can automatically
learn relevant features [17], are capable of handling large and complex
datasets and are highly adaptable. Despite advancements in deep learning
algorithms, there are some challenges that need research focus. The first
challenge is the lack of large annotated datasets, particularly for rare
diseases. Using deep learning on small datasets will lead to overfitting of
the model. Hence, learning a reliable and robust model with a small number of
samples is another challenge. Existing techniques are ineffective in dealing
with such scenarios where retraining on new data is required. Due to the small
number of available samples, existing techniques may not be able to generalize
effectively, resulting in lower performance.

The research community is focusing on developing approaches to overcome the
aforementioned challenges. One line of research is the few-shot learning-based
approaches. These approaches can be meta-learning-based [18], metric-based
[19, 20], or optimization-based [21]. These approaches are gaining popularity
because the model generalizes well to fewer data samples. Despite achieving
success in a variety of tasks, an automated medical diagnosis system ought to
be transparent, comprehensible, and explainable. One of the most desired
properties is explainability [22]. Explainability is referred to as the
process of examining the decisions that the system has made. It should be able
to justify the reasoning behind any decision. The end-user or health
professional should be able to retrace the decisions that inculcate trust
among them. Additionally, it is important to demonstrate features in such a
way that is fully understandable to non-deep learning users, such as medical
professionals.

Continuing in the path of dealing with low data regimes and introducing
explainability, this work aims to present a multitask explainable few-shot
learning-based approach by fusing segmentation and classification tasks for
skin lesions. In this work, we use prototypical networks, a metric-based few-
shot learning approach. This approach is more straightforward, robust and
computationally efficient as compared to other approaches. To extract the ROI
from the input image, we have added a segmentation step in our proposed
approach that will extract the ROI from the input image and enable the
classification model to focus on only that region. We have also added visual
descriptions of the decisions made by the system that aid in comprehending the
regions that were focused on while making any decision.

To summarise, we propose a multitask few-shot learning technique for skin
lesion classification that fuses segmentation and classification tasks in a
unified manner. The following are the key contributions of this paper:

  * 1.

We propose a prototypical few-shot learning-based end-to-end network for the
skin lesions

  * 2.

The proposed framework combines the segmentation and classification in such a
way that the artefacts are excluded before classifying the input image

  * 3.

We evaluate the performance of the proposed method in comparison to the Cosine
(Cos) and Euclidean (Euc) distances

  * 4.

Cross-database experiments are conducted utilising the HAM10000, Derm7pt and
PH2 datasets to illustrate the generalizability of the proposed approach.

  * 5.

Explainability of the proposed approach is illustrated using GradCAM results.

##  2 Related Work

This section provides a brief overview of approaches developed for segmenting
and classifying skin lesion images.

###  2.1 Skin Lesion Segmentation

Jafari et al. [23] have proposed using a guided filter as a pre-processing
filter and extracted local and global patches based on fixed window size to
classify the pixel as a lesion or normal. Xue et al. [24] have proposed an
adversarial-based skin lesion segmentation network. In this work, the authors
have proposed a multi-scale L1 loss function that learns global and local
features by capturing long- and short-range pixel relationships. Wei et al.
[25] conducted a similar research in which the authors utilised an attention
module that automatically suppresses irrelevant features and focuses just on
lesion features. In addition, the authors propose a new loss function that
combines the Jaccard distance loss and the adversarial feature mapping loss.
Experiments show that their proposed method produces precise masks with sharp
boundaries. A GAN-based strategy for skin lesion segmentation has also been
proposed by Tu et al. [26]. The authors employed a network with a Dense-
residual block to help in feature propagation and reuse and multi-scale
feature mapping from several layers. Bi et al. [27] have developed a stacked
adversarial learning technique for segmenting skin lesions. The authors
iteratively learned class-specific features to maximise feature diversity and
added them to the FCN training data. The results of the experiments show that
this method enhanced segmentation accuracy.

Mishra et al. [28] have proposed an algorithm for skin lesion segmentation
using the concept of U-Net architecture with fewer layers. The authors have
compared their results with otsu’s thresholding method and concluded that deep
learning gives better results. SkinNet, a modified version of U-Net, is
proposed by Vesal et al. [29]. The architecture employed dilated and densely
block convolutions during training to incorporate multiscale and global
information from the input image. The results show that their proposed method
can handle the poor contrast between the lesion and healthy tissue. Hasan et
al. [30] proposed the DSNet, a semantic segmentation network for skin lesions.
To reduce the number of parameters, the authors used dense blocks in the
encoder and depth-wise separable convolutions in the decoder. As a result, a
lightweight segmentation network with good segmentation results is developed.
Song et al. [31] have proposed an end-to-end approach for skin lesion analysis
where they jointly performed detection, segmentation, and classification using
focal loss and Jaccard distance to improve the class imbalance and
segmentation performance. CMM-Net is a network proposed by Al-Masni et al.
[32] for biomedical image segmentation. In the UNet design, the authors have
combined the global contextual features of multiple spatial scales.

Khadga et al. have proposed an optimization-based few-shot approach for
medical image segmentation [33, 34]. The authors suggested employing bi-level
optimization to solve the vanishing gradient problem. During training, a
compound loss function consisting of log-cosh-dice and binary cross-entropy
loss is used. The results of the experiments show that the proposed method
performs well and has a higher generalisation ability. Zhang et al. [35] have
proposed an approach to extract rich embedding features like global embedding,
peak embedding, adaptive embedding and a depth-priority context module in one-
shot semantic segmentation. To incorporate the prior knowledge from support
and query images, Xiao et al. [36] proposed an approach for skin lesion
segmentation for low-data regimes. The authors have extracted the prior mask
from the samples (support and query) that helps discard the background and
improve the segmentation performance. Sun et al. [37] have proposed a
transformer-based approach for the few-shot semantic segmentation. The authors
have employed transformer blocks to extract global information and
convolutional layers for local information. In continuation to this, Shen
et.al. [38] proposed to use semi-supervised few-shot semantic segmentation
where the authors have used poisson learning to model the relationship between
labelled and unlabeled samples. Also, the authors have used spatial
consistency, which further improves the performance.

###  2.2 Skin Lesion Classification

Various studies have been conducted that utilizes transfer learning while
classifying skin lesions [39, 40, 41]. The authors have used deep
architectures and feature maps of various layers while designing the approach.
These approaches demonstrate the feasibility of using deep learning in
classifying skin lesions. Yu et al. [42] has proposed a method for detecting
melanoma. The authors used a deep residual network and Fisher Vector encoding
to generate a global feature representation. The authors employed SVM as a
classifier, and experimental studies indicate the efficacy of their proposed
method. Huang et al. [43] have proposed MelanomaNet as a melanoma detection
architecture to boost feature diversity. To model the relationship between
feature channels, the authors used the Inception-v4 network and included a
residual-squeeze-and-excitation (RSE) block. The authors employed SVM with RBF
kernel for classification. Gessert et al. [44] have proposed an approach for
addressing resolution and class-imbalance issues in dermoscopic skin lesions.
The authors propose a new patch-based attention mechanism that explicitly
models local and global information. To resolve the class imbalance, the
authors used a loss function, diagnosis-guided loss weighting, and providing
more weight to hard samples.
To use an ensemble approach to classify skin images, Harangi et al. [45] have
proposed the use of an ensemble consisting of deep architectures such as
AlexNet, VGGNet and GoogLeNet. Tang et al. [46] used ensemble learning to
develop a skin lesion classification approach that included both global and
local information from the input image. Adegun et al. [47] have proposed a
deep-learning approach to automatically segment and classify skin lesion
images. The authors have used multi-scale encoder-decoder architecture and
FCN-based DenseNet for segmentation and classification. Rodrigues et al. [48]
have proposed an IOT-based solution for the classification of skin lesions by
using a web service called LINDA, which performs all the computations over the
cloud. Qin et al. [49] have presented the use of style-based GAN to generate
new data for training in skin lesion classification. Other approaches have
posed skin lesion analysis as a few-shot problem and tried to design methods
that work well with fewer data.
To improve the existing prototype-based method for skin lesion classification,
Zhuet. al. [50] proposed a temperature network that generates query-specific
prototypes leading to compact intra-class distributions. In this direction,
Roy et. al. [51] proposed to evaluate each sample’s influence (maximum mean
discrepancy) with mean embedding on the sample distribution of the particular
class. To further strengthen the concept of using prototypes, Prabhu et al.
[52] have proposed a prototypical clustering network for dermatological
conditions. They selected more than one prototype for a class and then refined
them during training. Mahajan et al. [53] have proposed a few-shot learning
approach for the identification of skin diseases. The authors have used group
equivariant convolutions (G-convolutions) instead of standard convolutions,
which improves the network’s performance. Li et al. [12] have proposed a meta-
learning-based approach for rare disease diagnosis. The authors have used a
dynamically scaled cross-entropy loss that automatically down-weights the easy
tasks and focuses on hard tasks. To use the attention mechanism for feature
extraction, Liu et. al. [54] proposed to use a relative position network (RPN)
and relative mapping network (RMN). The authors used RPN to extract features,
and RMN was used to obtain the similarity during the classification process.
The aforementioned algorithms yield good accuracy on the task at hand.
However, they only perform segmentation or classification, and none of them
integrates the two tasks in an end-to-end manner for the low data regime
scenario. Also, limited algorithms incorporate explainability in few-shot
learning-based algorithms for skin lesion analysis. To the best of our
knowledge, this is the first paper to propose an explainable few-shot
learning-based end-to-end system for skin lesion analysis that employs a
segmentation mask as attention while classifying the input images.

##  3 Methodology

This paper presents a multitask deep learning few-shot-based appoach for skin
lesion classification. The steps involved in the proposed network are
summarized in Fig. 3.

###  3.1 Problem Definition

The problem is to train a multi-task model that can jointly perform skin
segmentation and classification on a dataset with a small number of labelled
samples. We have adopted a prototypical-based approach for classification and
an encoder-decoder-based architecture for segmentation.

###  3.2 Multitask Network for Segmentation and Classification

Figure 2: Illustrating the basic working of prototypical networks

This section explains the working of the proposed approach.

####  3.2.1 Segmentation Network

A segmentation model is generally an encoder-decoder-based architecture where
the encoder outputs the encoding, and the decoder uses it to construct the
prediction binary mask. In this work, for segmentation, we have used UNet [55]
as it has shown significant performance in medical images [56]. Almost all
skin lesion images have one region that has to be classified; hence, the
segmentation model is trained using skin lesion images only. We train the
segmentation network using the skin lesion images from publicly available
datasets, including those used in this work. For each image $X_{i}$,
segmentation loss is computed between the predicted mask and the ground truth
mask using Eq. 3.2.1.

$L_{s}=-\sum_{x,y}[\mathbf{y}_{i}(x,y)\log(\widehat{\mathbf{y}}_{i}(x,y))]+(1-\mathbf{y}_{i})\log(1-\widehat{\mathbf{y}}_{i}(x,y))$

where $L_{s}$ is the segmentation loss for image $X_{i}$, $y_{i}(x,y)$ and
$\widehat{y}_{i}(x,y)$ are the pixel value of ground truth and predicted masks
at point (x,y), respectively.

####  3.2.2 Classification Network

This work follows the concept of the prototypical approach for classification
as in [19]. Fig. 2 illustrates the basic working of such networks.

Similar to previous few-shot learning-based approaches [21, 19, 57], we also
follow an episodic way of learning. In episodic learning, each few-shot task
is treated as an episode, meaning the model must learn to recognize novel
classes based on only a few examples.

In prototypical networks, features are extracted from support and query images
which act as the input to the rest of the pipeline. For each class in the
support set, a representative vector (mean feature vector) is computed as the
class prototype, and a distance is calculated between the query and the mean
vectors. To predict the class label of the query image, the query image is
assigned to the class with the closest distance, and a softmax activation
function is used to get the class probabilities. Formally, the problem can be
defined as:

Let $\mathcal{L}=\left\\{l_{1},\ldots,l_{x}\right\\}$ be the set of seen
classes with a large number of labelled samples and
$\mathcal{N}=\left\\{n_{1},\ldots,n_{y}\right\\}$ be the set of new and rare
classes with fewer labelled samples, where $x$ and $y$ are the total number of
samples in $\mathcal{L}$ and $\mathcal{N}$, respectively. The two sets are
disjoint i.e., $\mathcal{L}\cap\mathcal{N}=\phi$. From $\mathcal{L}$, we can
construct a large number of classification tasks by repeated sampling without
replacement. Each task consists of a support set and a query set. The aim is
to predict the labels of the query images based on the support images.

Figure 3: Showcasing the proposed multitask approach that fuses the
segmentation and few-shot-based classification

###  3.3 Fused Network

This section presents the fusion of the two networks to obtain a unified
pipeline. The segmentation model helps to focus on the region of interest and
is used in two experiments.

  * 1.

(E1) The segmentation model is frozen (no training done) and used in the fused
network to generate masks without additional refinement.

  * 2.

(E2) We freeze some layers of the pretrained segmentation model and only train
the last layer with the classification task to predict the segmentation masks.
These masks are refined during back-propagation of the segmentation loss. This
step helps to improve the classification accuracy.

The overall working of the fused network is as follows: The input images and
ground truth masks pass through the segmentation network. The prediction masks
are generated for the images. For each predicted mask, segmentation loss is
calculated using Eq. 3.2.1. The predicted mask is then used to extract the ROI
from each respective input image using pixel-wise multiplication. These images
are fed to the classification network, where they are divided into support and
query images. In this work, ResNet50 [58] pre-trained on ImageNet is employed
as the backbone for feature encoding. Episodic training is followed to learn
the embedding function by optimizing the classification loss. To compute the
distance between query and class prototypes, we have used euclidean and cosine
distances, which are given in Eq. 1 and 2. Let Q represent the query image,
and M_i represent the mean vector of the ith class. The Euclidean distance
(EUC) is calculated as follows:

$EUC(Q,M\\_i)=\sqrt{\sum_{i=1}^{n}(Q_{i}-M_{i})^{2}}$ (1)

and the Cosine distance is calculated as:

$CD(\theta)=\dfrac{Q\cdot M\\_i}{\|Q\|_{2}\|M\\_i\|_{2}}$ (2)

The classification loss is computed by taking log-softmax over the computed
distances. Eq. 3 gives the class probability of the query sample x and the
prototype of each class $p_{k}$. Learning is performed by minimizing the
negative log-probability of distance via the optimizer. The classification
loss is computed as,

$p_{\theta}(y=k\mid\mathbf{x})=\frac{\exp(-d(f_{\theta}(\mathbf{x}),\mathbf{p}_{k}))}{\sum_{k^{\prime}}\exp(-d(f_{\theta}(\mathbf{x}),\mathbf{p}_{k^{\prime}}))}$
(3) $L_{c}=-\log p_{\theta}(y=k\mid\mathbf{x})$ (4)

The final segmentation loss that gets backpropagated is calculated by adding
the segmentation loss with the classification loss given in Eq. 5. Here, the
value of $\lambda$ is set to 2.

$L_{total}=L_{s}+\lambda\times L_{c}$ (5)

The classification loss is given more weightage, so the segmentation mask will
be more refined. This way, the classification performance gets improved, and
the same can be seen in experimental results (Tables 1, 3, and 2).

During testing for new and rare classes, the trained feature extractor is used
for encoding, and the same episodic learning is used. The performance of the
approach is evaluated using average accuracy, which is computed over the
samples from the randomly chosen classes (which can be less than or equal to
the number of new and unseen/rare classes).

##  4 Experimental Setup

This section presents the dataset description, implementation details and
experimental results for the experiments.

###  4.1 Dataset Description and Preparation

The proposed approach is evaluated on the three benchmark dermatological
datasets, viz., HAM10000 [59], PH2 [60], and Derm7pt [61]. These datasets are
chosen because they consist of fewer samples that can act as unseen/rare
classes during testing.
HAM10000 contains 10,015 dermatoscopic images corresponding to seven classes -
Melanoma (1113), Melanocytic Nevi (6705), Basal Cell Carcinoma (514), Actinic
Keratoses and Intraepithelial Carcinoma (327), Dermatofibroma (115), Benign
Keratosis (1099), and Vascular lesions (142).
PH2 consists of a set of 200 dermoscopic images corresponding to 3 classes -
Common nevi (80), Atypical nevi (80), and Melanoma (40).
Derm7pt contains over 2000 (clinical and dermoscopy) images pertaining to 20
classes. We have used the dataset split mentioned in [53].
Data Preparation: Out of the seven classes in HAM10000, we selected four
classes with more samples (Melanoma, Benign Keratosis, Melanocytic Nevi, Basal
Cell Carcinoma) in the training set. The testing set included the remaining 3
classes (Actinic Keratoses and Intraepithelial Carcinoma, Dermatofibroma, and
Vascular Lesions). To verify the generalizability of the proposed approach, we
used the PH2 dataset and test classes of Derm7pt only during evaluation to
perform the cross-database experiments. All the images were normalized and
resized to (224,224,3) to reduce the computational cost. We used several
augmentation techniques on the input training images, such as vertical and
horizontal flipping (with p = 0.5) and colour jittering, to add variation in
the training data. The performance of the model is evaluated using $k-$way
$n-$shot learning protocols. Particularly, in our experiments, $k=$ 2 classes
with varying $n=$ 1, 3 and 5. In other words, the support set comprises $k$=2
classes and

  * 1.

for each class, if the number of samples is 5, it is referred to as the 5-shot
setting

  * 2.

for each class, if we have 10 samples each, it is referred to as the 10-shot
setting

For comparison with the baseline, the proposed model is also evaluated on
3-way with 5-shot and 10-shot settings for the classification task.

###  4.2 Implementation Details

The proposed framework is implemented in python on the PyTorch library. The
models have been trained and tested using Nvidia 1080Ti GPU. As a preliminary
step for the segmentation model, we take UNet [55] architecture pre-trained on
ImageNet [62] and finetuned it using publicly available skin datasets. In the
fused multitask network, we trained only the last layer of the network. Eq.
3.2.1 is used to calculate the loss between the predicted mask and the ground
truth mask. For classification, we take ResNet50 [58] as the backbone network
for feature extraction. We used Adam as the optimizer, and the learning rate
is set as 1e-3 for segmentation and 1e-6 for the classification network. We
jointly train segmentation and classification networks for 10 epochs with 100
few-shot tasks each. For each experiment, all images are resized to
(224x224x3) to reduce the computation.

###  4.3 Experimental Results

The performance of the proposed approach is evaluated for 3 benchmark
dermatological datasets. For evaluation, we have used average accuracy (%) as
the performance metric. During the testing phase, the images are distributed
equally across all the classes so that the accuracy does not get affected by
the class imbalance. The reported accuracy is the average accuracy across 100
few-shot tasks sampled from the test set. The model is evaluated by varying
$k$ as 1, 3 and 5, and the distance metric as cosine and euclidean distance
for the 2-way classification task.
Comparative results for various models on HAM10000, PH2 and Derm7pt are
summarized in Tables 1, 3, 2 respectively. The average accuracy for all the
datasets goes on increasing as the value of $k$ increases. This can be
attributed to the fact that more samples provide more information to represent
the underlying distribution and hence can estimate the class prototype in a
better way. For skin lesions, it is observed that the proposed method
outperforms other methods on all three datasets. In case of HAM10000, the
average accuracy for 1, 3 and 5 shots get improved by around 2%; in PH2, there
is a significant improvement of average accuracy from 70.98% (ProtoNet with
Cosine distance) to 76.06% (Proposed Network with Euclidean distance) for $k$
= 5; for $k$ =1 and 3, the proposed network also outperforms the other
networks. For the Derm7pt dataset, the proposed approach is also able to
outperform the other methods. It is quite evident from the experimental
results that both cosine distance and euclidean distance are effective
distance metrics for few-shot prototypical-based classification networks in
skin lesion classification.

As a baseline experiment, a pre-trained ResNet50 model is trained on the
training data (4 classes from HAM10000, same as used in the proposed
approach). All the parameters, such as learning rate, epochs, and optimizer,
are kept the same for a fair comparison. For testing, the model is fine-tuned
for the rare classes (same as in the proposed approach). The average accuracy
for the test classes of HAM10000, PH2 and Derm7pt is shown in Fig. 4. This
accuracy is low compared to the accuracy obtained by the proposed approach on
3-way classification. This behaviour is quite explainable as the pre-trained
model is trained on classes different than classes at test time. The trained
model is biased towards the training data and does not adapt to the new unseen
data. On the other hand, the proposed approach is able to generalize well for
the unseen classes as it tries to learn the metric space that helps to better
classify the images. This demonstrates the limitation of transfer learning of
deep models to adapt to new unseen data. We have also reported the results in
terms of confidence intervals. Table 4 summarizes the results for the datasets
by varying the values of CI as 70, 90 and 95%. The margin error decreases with
the number of shots (samples). The margin error is used to measure the
confidence of the model in its predictions. The larger margin error indicates
lower confidence and vice versa. As the number of shots increases, the model
gets more confident about the predictions; hence the margin of error is low.
In the HAM10000 dataset, the margin error decreases from 0.51 to 0.43, 1.06 to
0.73 for 75% and 95% CI, respectively, indicating the model is getting more
confident about the predictions. Generally, as we increase the CI, the width
of CI will also increase and result in a higher margin of error. However,
other factors, such as data and sample size variability, also affect the
margin of error and can be seen in the behaviour of 90% CI for the HAM10000
dataset. In future, we will try to build a more resilient and robust system
that will have a lower margin of error.

Table 1: Comparison of Average Accuracy (in %) on HAM10000 dataset for 2-way
classification (shots = 1,3,5)

Model | Distance | 1 | 3 | 5
---|---|---|---|---
Relation Net [20] | - | 49.65 | 50.5 | 56.35
Matching Net [57] | - | 65.9 | 73.3 | 75.7
| MDD [53]
---
- | 64.5 | 73.5 | 79.7
| ProtoNet [19]
---
Cosine | 64.47 | 72.91 | 77.71
| ProtoNet [19]
---
Euclidean | 60.22 | 70.04 | 76.35
E1 | Cosine | 62.39 | 71.72 | 75.17
| Proposed
---
Euclidean | 62.65 | 73.12 | 77.57
| Proposed
---
Cosine | 62.24 | 73.85 | 78.21

Table 2: Comparison of Average Accuracy (in %) on Derm7pt dataset for 2-way
classification (shots = 1,3,5)

Model | Distance | 1 | 3 | 5
---|---|---|---|---
Relation Net [20] | - | 50.35 | 50.25 | 49.65
Matching Net [57] | - | 59.30 | 66.55 | 65.60
| MDD [53]
---
- | 64.1 | 66.8 | 69.5
SCAN [63] | - | 65.27 | - | 77.97
| ProtoNet [19]
---
Cosine | 60.68 | 70.74 | 76.54
| ProtoNet [19]
---
Euclidean | 60.62 | 70.80 | 69.91
E1 | Cosine | 59.48 | 59.95 | 59.95
| Proposed
---
Euclidean | 59.95 | 71.71 | 77.64
| Proposed
---
Cosine | 62.70 | 71.71 | 77.83

Table 3: Comparison of Average Accuracy (in %) on PH2 dataset for 2-way
classification (shots = 1,3,5)

Model | Distance | 1 | 3 | 5
---|---|---|---|---
Relation Net [20] | - | 50.45 | 49.60 | 51.10
Matching Net [57] | - | 59.75 | 65.0 | 64.05
| ProtoNet [19]
---
Cosine | 61.59 | 68.88 | 70.98
| ProtoNet [19]
---
Euclidean | 57.57 | 66.82 | 69.91
E1 | Cosine | 61.31 | 68.39 | 71.86
| Proposed
---
Euclidean | 59.86 | 68.89 | 76.06
| Proposed
---
Cosine | 63.65 | 70.81 | 75.85

Figure 4: Comparing the performance of the proposed approach (with cosine
distance) with baseline (transfer learning) for 3-Way and 5-way
classification. Each set of bars corresponds to the average accuracy on
HAM10000, PH2 and Derm7pt, respectively.

###  4.4 Visualization results

We have reported some segmentation visualisations in Fig. 5. The proposed
algorithm can produce a segmentation mask that is quite similar to the ground
truth mask. This refinement of masks, even with fewer samples, can be
attributed to utilising classification loss in the segmentation network. This
segmentation phase enables the algorithm to avoid the artefacts in the image
and focus just on the affected area. However, as shown in Fig. 6, images with
fading borders and low contrast require closer attention.

Figure 5: Showing the images (a), their ground truth masks (b) and the
predicted masks (c) by the segmentation network while training the last layer
of it in parallel to the classification network. Figure 6: Showcasing some of
the correctly and misclassified samples for the three datasets.

To further support the use of the segmentation phase, we have also used
GradCAM [64] to showcase the visual explanations. By highlighting the areas on
which the algorithm focuses while making a decision, it aids in explaining the
decisions made by the proposed approach. As observed in Fig. 7, the proposed
approach focuses only on the affected region. However, without segmentation,
the algorithm examines a variety of unaffected visual components and,
therefore, of little interest. This can also be seen in Fig. 7. Since there
are fewer test samples, the proposed approach focuses on the smallest region
that discriminates the samples in a better way. This explains the smaller
portion being highlighted in the GradCAM results. These explanations help the
end-users to get a better idea of the working of the algorithm and increase
the credibility of any AI algorithm.

Figure 7: Visualization of the GradCAM results on the samples of the three
datasets. The top row represents the input image, the middle row represents
the GradCAM results without segmentation, and the bottom row represents the
GradCAM results of the proposed approach. The red areas represent the regions
that were taken into account while making a decision (Best viewed in color).
Table 4: Comparison of Average Accuracy (in %) over 1000 randomly generated
episodes with varying confidence interval (75, 90 and 95) on HAM10000 (HAM),
Ph2, and Derm7pt dataset for 2-way classification (shots = 1,3,5)

| CI/Datasets
---
75 | 90 | 95
HAM | 1 | 62.65 $\pm$ 0.51 | 62.65 $\pm$ 0.55 | 62.65 $\pm$ 1.06
3 | 73.12 $\pm$ 0.47 | 73.12 $\pm$ 0.67 | 73.12 $\pm$ 0.80
5 |  77.57 $\pm$ 0.43 |  77.57 $\pm$0.61 |  77.57 $\pm$ 0.73
Ph2 | 1 | 59.86$\pm$ 0.62 | 59.86 $\pm$ 0.89 | 59.86 $\pm$ 1.06
3 | 68.89 $\pm$ 0.59 | 68.89 $\pm$ 0.84 | 68.89 $\pm$ 1
5 |  76.06 $\pm$ 0.57 |  76.06 $\pm$ 0.82 |  76.06 $\pm$ 0.98
Derm7pt | 1 | 59.95 $\pm$ 0.62 | 59.95 $\pm$ 0.89 | 59.95 $\pm$ 1.06
3 | 71.72 $\pm$ 0.6 | 71.72 $\pm$ 0.85 | 71.72 $\pm$ 1.02
5 |  77.64 $\pm$ 0.56 |  77.64 $\pm$ 0.80 |  77.64 $\pm$ 0.96

###  4.5 Ablation Study

To study and investigate the effect of varying feature extractors and the
value of $\lambda$, we have performed various experiments to showcase the
effect on classification accuracy.

####  4.5.1 Effect of Feature Extractor

By varying the backbone for feature extraction with fewer layers (VGG16,
ResNet18) and a large number of layers (DenseNet-121), the proposed algorithm
is evaluated. Table 5 presents the reported results for the 5-shot 2-way
setting. The results are clearly inferior to those shown in Tables 1, 3 and 2.
The smaller sample size in each rare class can be attributable to this
pattern. The prototypical networks are based on the calculation of a mean
/representative vector with which the distances are computed. Choosing the
right architecture helps to make the approach more efficient. Less layered
architecture does not allow for the selection of more distinctive features,
and more layered architecture reduces generalizability due to overfitting.
Based on the results, we opted to use ResNet50 as the backbone for feature
extraction in this work.

####  4.5.2 Effect of $\lambda$ in loss function

We evaluated the proposed algorithm by changing the value of $\lambda$ in 5 in
order to examine the impact of combining the classification loss and
segmentation loss. The results are reported in Table 6.

Table 5: Average Accuracy (in %) by varying feature extractor for 5-shot 2-way classification Feature Extractor | HAM10000 | PH2 | Derm7pt
---|---|---|---
VGG16 [65] | 73.79 | 66.33 | 68.37
ResNet18 [58] | 76.50 | 74.35 | 75.05
DenseNet-121 [66] | 74.21 | 73.95 | 72.23

As we can see from the results, combining the classification loss with the
segmentation loss improves the classification accuracy. The misclassification
directs the segmentation network to refine the mask, which acts as attention
for the classification network to focus only on the affected segmented region.
This step helps in improving both classification and segmentation performance.
The accuracy, however, declines if we increase the weight of classification
loss or the value of $\lambda$. It may be because fewer samples are available,
and giving classification loss more weight results in poor generalizability.

Table 6: Average Accuracy (in %) by varying the value of $\lambda$ for 5-shot 2-way classification $\lambda$ | HAM10000 | PH2 | Derm7pt
---|---|---|---
1 | 77.50 | 74.79 | 76.30
2 | 78.21 | 75.85 | 77.83
3 | 77.37 | 75.02 | 76.45
4 | 77.25 | 74.89 | 76.32

##  5 Conclusion

Skin cancer is one of the most serious skin diseases. Skin lesions that are
detected early can help prevent complications, including death. Because the
trained model becomes biased towards the classes seen during training, the
transfer learning approach does not generalise well to new and unseen classes.
This behaviour can also be observed in medical settings, where new classes are
introduced with a small sample size. This paper presents a multitask few-shot
learning-based network for skin lesion classification. In the proposed
approach, the segmentation and classification tasks are fused into a single
pipeline. We evaluated the proposed network using Cosine and Euclidean
distances, and it was proved from the experimental results that both distances
are effective in the case of classifying skin lesions. Experiments with the
HAM10000, PH2 and Derm7pt datasets show that the proposed method yields good
accuracies even when applied across databases, exhibiting generalizability.
Any skin disease can benefit from the proposed approach. To further improve
the performance in future, we plan to propose a more robust segmentation
network with varying loss functions. Moreover, we intend to expand our
research to other medical modalities with limited data in the future.

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