11institutetext: National Tsing Hua University, Hsinchu, Taiwan
22institutetext: University of Notre Dame, IN, USA 33institutetext: The
Chinese University of Hong Kong, Hong Kong
33email<EMAIL_ADDRESS>
# A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor
Segmentation

Yu-Jen Chen 11 Yiyu Shi 22 Tsung-Yi Ho 33

###### Abstract

Magnetic resonance imaging (MRI) is a commonly used technique for brain tumor
segmentation, which is critical for evaluating patients and planning
treatment. To make the labeling process less laborious and dependent on
expertise, weakly-supervised semantic segmentation (WSSS) methods using class
activation mapping (CAM) have been proposed. However, current CAM-based WSSS
methods generate the object localization map using internal neural network
information, such as gradient or trainable parameters, which can lead to
suboptimal solutions. To address these issues, we propose the confidence-
induced CAM (Cfd-CAM), which calculates the weight of each feature map by
using the confidence of the target class. Our experiments on two brain tumor
datasets show that Cfd-CAM outperforms existing state-of-the-art methods under
the same level of supervision. Overall, our proposed Cfd-CAM approach improves
the accuracy of brain tumor segmentation and may provide valuable insights for
developing better WSSS methods for other medical imaging tasks.

###### Keywords:

Tumor segmentation Weakly-supervised semantic segmentation

##  1 Introduction

Deep learning [5, 26, 8, 6, 7] has shown great potential in various of
application for medical images. Among all of them, medical image segmentation
is a critical task in disease diagnosis and assessment. Most existing
approaches automating this process by extracting the location information of
specific tissues or substances. However, most deep-learning-based approaches
require fully or partially labeled training datasets, which can be time-
consuming and expensive to annotate.

To address this issue, recent efforts have focused on developing segmentation
frameworks with little or no segmentation labels. Weakly-Supervised Semantic
Segmentation (WSSS) has emerged as a promising approach that utilizes weak
supervision, such as image-level classification labels. The majority of works
in this direction have centered around Class Activation Mapping (CAM) [29],
which was originally developed as a technique for neural network
visualization.

However, existing CAM-based WSSS methods [29, 22, 18, 24, 21, 25, 16, 28, 23]
generate the activation map from internal neural network information, which
can lead to suboptimal solutions. For example, Grad-CAM[22] calculates the
gradients between the target class confidence and the feature map and uses
them as weights. Wang et al. [24] proposed the ScoreCAM, which showed that
feature maps with higher weights may still have lower contributions to the
prediction score.

In this paper, we propose a confidence-induced CAM (Cfd-CAM) for brain tumor
segmentation in magnetic resonance imaging (MRI). Unlike recent CAM methods,
Cfd-CAM uses the classification confidence of the input image being masked by
its feature maps to estimate the importance of each feature map properly. Our
code is publicly available at
https://github.com/windstormer/Cfd-CAM

Experimental results on the 2021 Brain Tumor Segmentation Challenge (BraTS
2021) [17, 2, 3] and the TCGA-LGG dataset [19] demonstrate the superiority of
Cfd-CAM over existing CAM methods in extracting segmentation results from
classification networks. To ensure a fair comparison, we also reproduce a
state-of-the-art unsupervised segmentation method and two supervised
approaches as the performance ceiling. Our proposed Cfd-CAM approach shows
great promise in reducing the reliance on fully or partially labeled training
datasets for medical image segmentation, and we believe it may have broader
applications in other fields.

##  2 Confidence-induced CAM (Cfd-CAM)

Figure 1: Illustration of the proposed Cfd-CAM method. FC stands for the
fully-connected layer.

The Cfd-CAM method (Fig. 1) involves a binary classification network, e.g.
ResNet-18 [11], trained to determine whether an image contains a tumor
(positive) or not (negative). For every test image, the trained classifier
generates the corresponding segmentation mask using the proposed Cfd-CAM.

Similar to most CAM methods, the aggregated activation map is a linear
combination of all the feature maps with a ReLU [1] operation that clips all
negative values to 0. However, to achieve accurate weights for feature maps,
our proposed Cfd-CAM method constructs a confidence-induced algorithm to fit
the contribution given by each feature map, which is introduced step-by-step
as follows:

(1) We load the pretrained weight of the classifier model and obtain the
feature maps of the input image from the last layer before global average
pooling.

(2) For each feature map, we upsample it to the original image size and apply
min-max scaling to normalize the value to [0,1]. Afterward, we mask every
processed feature map to the original image.

(3) All the masked images are fed into the classification model to obtain its
confidence. The importance of each feature map is determined as follows: if
the confidence of the masked image is lower than the confidence of a blank
image, then the result of the classification network, including the feature
maps, shall not be trusted. In this case, we simply set the weight to $0$.
Otherwise, the weight of the feature map is set as the confidence of the
masked image. To preserve the value of the generated map ranged [0,1], we
apply the proportion to the weight for feature map aggregation.

(4) With the weight of each feature map determined, the aggregated activation
map is generated.

(5) The proposed Cfd-CAM uses the activation maps obtained from the original
image and the double-sized image for the experiment. To combine the knowledge
given from different resolutions, the final activation map is calculated by
the average of two activation maps, the original-sized and the double-sized
(rescaled back to the original size).

(6) To generate the segmentation mask, we apply the Dense Conditional Random
Field (DenseCRF) algorithm [15] to determine the pixel-wise prediction from
the activation map.

Figure 2: Illustration of the weight modification of the previous work
(softmax) and the proposed Cfd-CAM. The weights highlighted in red show the
main difference of our Cfd-CAM.

To demonstrate the effectiveness of our proposed Cfd-CAM, we provide an
example to illustrate how it improves upon the previous method, as depicted in
Fig. 2. We consider a binary classification network that has three feature
maps extracted before global average pooling. Assume the logits of the input
masked by each feature map are ${7.8,10.0,-1.6}$, respectively. If we were to
use softmax or sigmoid activation with percentage proportions for the weight
of each map, the results would be ${0.09,0.90,0.00}$ and ${0.5,0.5,0.00}$,
respectively.

In the former method, the second feature map dominates the final activation
map. However, the latter method suggests that the classification network
should have similar confidence for the input masked by the first feature map
and that by the second, implying that their contribution to the activation map
should be equivalent.

It is more logical to use the network’s confidence directly for the input
masked by each feature map as the weight of that feature map. This approach is
the basis of our proposed Cfd-CAM and shows its superiority over the previous
method.

##  3 Experiments

###  3.1 Dataset

We evaluate our experiments on two MRI lesion segmentation datasets, the Brain
Tumor Segmentation challenge (BraTS) [17, 2, 3] and the Cancer Genome Atlas
Lower-Grade Glioma collection (TCGA-LGG) [19].

The Brain Tumor Segmentation challenge (BraTS) dataset [17, 2, 3] contains
2,000 cases, each of which includes four 3D volumes from four different MRI
modalities: T1, post-contrast enhanced T1 (T1-CE), T2, and T2 Fluid Attenuated
Inversion Recovery (T2-FLAIR), as well as a corresponding segmentation ground-
truth mask. The official data split divides these cases by the ratio of 8:1:1
for training, validation, and testing (5,802 positive and 1,073 negative
images). In order to evaluate the performance, we use the validation set as
our test set and report statistics on it. We preprocess the data by slicing
each volume along the z-axis to form a total of 193,905 2D images, following
the approach of Kang et al. [13] and Dey and Hong [9]. We use the ground-truth
segmentation masks only in the final evaluation, not in the training process.

The TCGA-LGG dataset [19] consists of 110 patients with 3,600 2D images
collected from the TCGA lower-grade glioma collection who had preoperative
imaging data available, containing at least a fluid-attenuated inversion
recovery (FLAIR) sequence. We follow the preprocess step and the split rate in
the BraTS dataset to separate the dataset into training, validation, and
testing (180 positive and 351 negative images) for experiments.

###  3.2 Implementation Details and Evaluation Protocol

We implement our method in PyTorch using a ResNet-18 model architecture for
the classifier. We pretrain the classifier using SupCon [14] and fine-tune it
in our experiments. Both pretraining and fine-tuning use the entire training
set. The initial learning rate of the classifier is set to 1e-4 and follows
the cosine annealing scheduler to decrease until the minimum learning rate is
5e-6, and we use the Adam optimizer. For model regularization, we set the
weight decay to 1e-5. To maintain a fair comparison, all classifiers are
trained until convergence with a test accuracy of over 0.9 for all image
modalities. Note that only class labels are available in the training set.

We use the Dice score and Intersection over Union (IoU) to evaluate the
quality of the semantic segmentation, following the approach of Xu et al.
[27], Tang et al. [23], and Qian et al. [20]. In addition, we report the 95%
Hausdorff Distance (HD95) to evaluate the boundary of the prediction mask.

##  4 Results

Table 1: Comparison with weakly-unsupervised methods (WSSS), unsupervised method (UL), and fully supervised methods (FSL) on BraTS dataset with T1, T1-CE, T2, and T2-FLAIR MRI images and the TCGA-LGG dataset. Results are reported in the form of mean$\pm$std. We mark the highest score among WSSS methods with bold text. Dataset | Type | Method | Dice $\uparrow$ | IoU $\uparrow$ | HD95 $\downarrow$
---|---|---|---|---|---
BraTS T1 | WSSS | Grad-CAM (2016) | 0.107$\pm$0.090 | 0.059$\pm$0.055 | 121.816$\pm$22.963
ScoreCAM (2020) | 0.296$\pm$0.128 | 0.181$\pm$0.089 | 60.302$\pm$14.110
LayerCAM (2021) | 0.571$\pm$0.170 | 0.419$\pm$0.161 | 23.335$\pm$27.369
Swin-MIL (2022) | 0.477$\pm$0.170 | 0.330$\pm$0.147 | 46.468$\pm$30.408
Cfd-CAM (ours) | 0.575$\pm$0.169 | 0.427$\pm$0.143 | 23.859$\pm$20.243
UL | C&F (2020) | 0.200$\pm$0.082 | 0.113$\pm$0.051 | 79.187$\pm$14.304
FSL | C&F (2020) | 0.572$\pm$0.196 | 0.426$\pm$0.187 | 29.027$\pm$20.881
Opt. U-net (2021) | 0.836$\pm$0.062 | 0.723$\pm$0.090 | 11.730$\pm$10.345
BraTS T1-CE | WSSS | Grad-CAM (2016) | 0.127$\pm$0.088 | 0.071$\pm$0.054 | 129.890$\pm$27.854
ScoreCAM (2020) | 0.397$\pm$0.189 | 0.267$\pm$0.163 | 46.834$\pm$22.093
LayerCAM (2021) | 0.510$\pm$0.209 | 0.367$\pm$0.180 | 29.850$\pm$45.877
Swin-MIL (2022) | 0.460$\pm$0.169 | 0.314$\pm$0.140 | 46.996$\pm$22.821
Cfd-CAM (ours) | 0.610$\pm$0.156 | 0.456$\pm$0.157 | 23.352$\pm$13.642
UL | C&F (2020) | 0.179$\pm$0.080 | 0.101$\pm$0.050 | 77.982$\pm$14.042
FSL | C&F (2020) | 0.246$\pm$0.104 | 0.144$\pm$0.070 | 130.616$\pm$9.879
Opt. U-net (2021) | 0.845$\pm$0.058 | 0.736$\pm$0.085 | 11.593$\pm$11.120
BraTS T2 | WSSS | Grad-CAM (2016) | 0.049$\pm$0.058 | 0.026$\pm$0.034 | 141.025$\pm$23.107
ScoreCAM (2020) | 0.530$\pm$0.184 | 0.382$\pm$0.174 | 28.611$\pm$11.596
LayerCAM (2021) | 0.624$\pm$0.178 | 0.476$\pm$0.173 | 23.978$\pm$44.323
Swin-MIL (2022) | 0.437$\pm$0.149 | 0.290$\pm$0.117 | 38.006$\pm$30.000
Cfd-CAM (ours) | 0.660$\pm$0.132 | 0.506$\pm$0.141 | 19.323$\pm$10.714
UL | C&F (2020) | 0.230$\pm$0.089 | 0.133$\pm$0.058 | 76.256$\pm$13.192
FSL | C&F (2020) | 0.611$\pm$0.221 | 0.474$\pm$0.217 | 109.817$\pm$27.735
Opt. U-net (2021) | 0.884$\pm$0.064 | 0.798$\pm$0.098 | 8.349$\pm$9.125
BraTS T2-FLAIR | WSSS | Grad-CAM (2016) | 0.150$\pm$0.077 | 0.083$\pm$0.050 | 110.031$\pm$23.307
ScoreCAM (2020) | 0.432$\pm$0.209 | 0.299$\pm$0.178 | 39.385$\pm$17.182
LayerCAM (2021) | 0.652$\pm$0.206 | 0.515$\pm$0.210 | 22.055$\pm$33.959
Swin-MIL (2022) | 0.272$\pm$0.115 | 0.163$\pm$0.079 | 41.870$\pm$19.231
Cfd-CAM (ours) | 0.733$\pm$0.126 | 0.594$\pm$0.153 | 15.273$\pm$8.251
UL | C&F (2020) | 0.306$\pm$0.190 | 0.199$\pm$0.167 | 75.651$\pm$14.214
FSL | C&F (2020) | 0.578$\pm$0.137 | 0.419$\pm$0.130 | 138.138$\pm$14.283
Opt. U-net (2021) | 0.914$\pm$0.058 | 0.847$\pm$0.093 | 8.093$\pm$11.879
TCGA-LGG | WSSS | Grad-CAM (2016) | 0.407$\pm$0.229 | 0.283$\pm$0.190 | 81.278$\pm$38.648
ScoreCAM (2020) | 0.490$\pm$0.222 | 0.351$\pm$0.184 | 57.821$\pm$30.275
LayerCAM (2021) | 0.378$\pm$0.211 | 0.255$\pm$0.171 | 69.696$\pm$42.493
Swin-MIL (2022) | 0.271$\pm$0.144 | 0.165$\pm$0.101 | 111.634$\pm$17.670
Cfd-CAM (ours) | 0.508$\pm$0.223 | 0.369$\pm$0.193 | 56.866$\pm$29.337
UL | C&F (2020) | 0.300$\pm$0.162 | 0.188$\pm$0.119 | 101.980$\pm$18.358
FSL | C&F (2020) | 0.637$\pm$0.216 | 0.502$\pm$0.224 | 46.925$\pm$17.889
Opt. U-Net (2021) | 0.668$\pm$0.196 | 0.532$\pm$0.209 | 47.035$\pm$19.529

###  4.1 Quantitative and Qualitative Comparison with State-of-the-art

Figure 3: Qualitative results of all methods using T2-FLAIR of BraTS dataset
(first row) and the TCGA-LGG dataset (second row). (a) Input Image. (b) Ground
Truth. (c) Grad-CAM [22] (d) ScoreCAM [24]. (e) LayerCAM [12]. (f) Swin-MIL
[20]. (g) Cfd-CAM (ours).

In this section, we evaluate the segmentation performance of the proposed AME-
CAM and compare it with four state-of-the-art weakly-supervised segmentation
methods: Grad-CAM [22], ScoreCAM [24], LayerCAM [12], and Swin-MIL [20]. To
provide a comprehensive comparison, we also include an unsupervised approach
C&F [4], its supervised version, and the supervised Optimized U-net [10],
which are non-CAM-based methods. Although the results from fully supervised
and unsupervised methods are not directly comparable to weakly supervised CAM
methods, we include them as interesting references for the potential
performance ceiling and floor of all the CAM methods.

Table 1 presents the quantitative comparison results. Grad-CAM and ScoreCAM
tend to overestimate the positive region and lead to false-positive
segmentation, resulting in low dice and precision scores in the BraTS dataset.
LayerCAM significantly improves the dice score to over 0.5 in the BraTS
dataset by capturing activation maps at different resolutions. Swin-MIL also
results in a dice score around 0.45, except for T2-FLAIR MRI in the BraTS
dataset. Compared with other weakly supervised segmentation methods, the
proposed Cfd-CAM achieves the highest dice score in all modalities of the
BraTS dataset and the TCGA-LGG dataset.

Compared to the unsupervised baseline (UL), C&F is unable to separate the
tumor and the surrounding tissue due to low contrast, resulting in low dice
scores in all experiments. With pixel-wise labels, the dice score of
supervised C&F improves significantly. Without any pixel-wise label, the
proposed Cfd-CAM outperforms supervised C&F in all modalities of the BraTS
dataset.

The fully supervised (FSL) Optimized U-net achieves the highest dice score and
IoU score in all experiments. However, even under different levels of
supervision, there is still a performance gap between the weakly supervised
CAM methods and the fully supervised state-of-the-art. This indicates that
there is still potential room for WSSS methods to improve in the future.

Figure 3 shows the visualization of the segmentation results from all five
weakly supervised approaches under two different datasets. Grad-CAM has the
worst performance with a large falsely segmented region. Most of the
boundaries are unacceptable due to the inappropriate construction of weights
by back-propagation. Cfd-CAM significantly reduces the overestimation of the
tumor area, resulting in improved weight construction through confidence. For
the TCGA-LGG dataset, all six methods cover the exact tumor area, except Swin-
MIL. The proposed Cfd-CAM results in the least area and fewer falsely
segmented regions.

Table 2: Ablation study of single- (ss) and multi-scale (ms) test of the proposed Cfd-CAM using both BraTS dataset and TCGA-LGG dataset. Results are reported in the form of mean$\pm$std. Dataset | Method | Dice $\uparrow$ | IoU $\uparrow$ | HD95 $\downarrow$
---|---|---|---|---
BraTS T1 | Cfd-CAM (ss) | 0.406$\pm$0.152 | 0.266$\pm$0.121 | 45.610$\pm$15.356
Cfd-CAM (ms) | 0.575$\pm$0.169 | 0.427$\pm$0.143 | 23.859$\pm$20.243
BraTS T1-CE | Cfd-CAM (ss) | 0.537$\pm$0.189 | 0.389$\pm$0.176 | 30.075$\pm$18.408
Cfd-CAM (ms) | 0.610$\pm$0.156 | 0.456$\pm$0.157 | 23.352$\pm$13.642
BraTS T2 | Cfd-CAM (ss) | 0.573$\pm$0.179 | 0.424$\pm$0.177 | 24.716$\pm$9.795
Cfd-CAM (ms) | 0.646$\pm$0.142 | 0.493$\pm$0.154 | 19.696$\pm$7.494
BraTS T2-FLAIR | Cfd-CAM (ss) | 0.599$\pm$0.221 | 0.462$\pm$0.221 | 24.604$\pm$14.609
Cfd-CAM (ms) | 0.698$\pm$0.159 | 0.557$\pm$0.181 | 17.675$\pm$9.698
TCGA-LGG | Cfd-CAM (ss) | 0.503$\pm$0.253 | 0.373$\pm$0.226 | 58.668$\pm$35.295
Cfd-CAM (ms) | 0.508$\pm$0.223 | 0.369$\pm$0.193 | 56.866$\pm$29.337

###  4.2 Ablation Study for Multi-scale Test

In this section, we compare three different tests: single-scale test using
original size, single-scale test using double the original size, and multi-
scale test that combines advantages from different scales. Fig. 4 shows that
the single-scale test using original size focuses on global vision and
produces large activation regions, while the single-scale test with double the
original size captures local and accurate boundaries, resulting in smaller
activation regions. The multi-scale test, as shown in Fig. 4(d), combines the
advantages provided by different scales and produces segmentation closest to
the ground truth.

Table 2 presents the quantitative comparison results for BraTS and TCGA-LGG
datasets, with the multi-scale test improving the segmentation dice score by
about 8% on average compared to the single-scale test using the original size.
Overall, the multi-scale test outperforms the single-scale tests, providing
more accurate segmentation.

Figure 4: Visualization of the activation map (top row) and ground-truth
segmentation mask (bottom row) of Cfd-CAM from (b) single-scale test of the
original size, (c) single-scale test of the doubled original size, and (d)
multi-scale test. (a) represents the input image and its ground-truth (GT)
segmentation mask.

##  5 Conclusion

We introduce Cfd-CAM, a novel brain tumor segmentation method for MRI images
that utilizes only class labels. By weighting feature maps with confidence,
our approach generates an activation map that overcomes limitations of other
CAM-based methods. Experimental results on the BraTS and TCGA-LGG datasets
show that Cfd-CAM outperforms other weakly-supervised segmentation approaches
based on CAM, indicating its effectiveness in accurately segmenting brain
tumors based solely on class labels.

## References

  * [1] Agarap, A.F.: Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375 (2018)
  * [2] Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data 4(1), 1–13 (2017)
  * [3] Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)
  * [4] Chen, J., Frey, E.C.: Medical image segmentation via unsupervised convolutional neural network. arXiv preprint arXiv:2001.10155 (2020)
  * [5] Chen, Y.J., Chang, Y.J., Wen, S.C., Shi, Y., Xu, X., Ho, T.Y., Jia, Q., Huang, M., Zhuang, J.: Zero-shot medical image artifact reduction. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). pp. 862–866. IEEE (2020)
  * [6] Chen, Y.J., Chang, Y.J., Wen, S.C., Xu, X., Huang, M., Yuan, H., Zhuang, J., Shi, Y., Ho, T.Y.: “one-shot” reduction of additive artifacts in medical images. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). pp. 836–841. IEEE (2021)
  * [7] Chen, Y.J., Shen, W.H., Chung, H.W., Chiu, J.H., Juan, D.C., Ho, T.Y., Cheng, C.T., Li, M.L., Ho, T.Y.: Representative image feature extraction via contrastive learning pretraining for chest x-ray report generation. arXiv preprint arXiv:2209.01604 (2022)
  * [8] Chen, Y.J., Tsai, C.Y., Xu, X., Shi, Y., Ho, T.Y., Huang, M., Yuan, H., Zhuang, J.: Ct image denoising with encoder-decoder based graph convolutional networks. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). pp. 400–404. IEEE (2021)
  * [9] Dey, R., Hong, Y.: Asc-net: Adversarial-based selective network for unsupervised anomaly segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 236–247. Springer (2021)
  * [10] Futrega, M., Milesi, A., Marcinkiewicz, M., Ribalta, P.: Optimized u-net for brain tumor segmentation. arXiv preprint arXiv:2110.03352 (2021)
  * [11] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
  * [12] Jiang, P.T., Zhang, C.B., Hou, Q., Cheng, M.M., Wei, Y.: Layercam: Exploring hierarchical class activation maps for localization. IEEE Transactions on Image Processing 30, 5875–5888 (2021)
  * [13] Kang, H., Park, H.m., Ahn, Y., Van Messem, A., De Neve, W.: Towards a quantitative analysis of class activation mapping for deep learning-based computer-aided diagnosis. In: Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment. vol. 11599, p. 115990M. International Society for Optics and Photonics (2021)
  * [14] Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D.: Supervised contrastive learning. arXiv preprint arXiv:2004.11362 (2020)
  * [15] Krähenbühl, P., Koltun, V.: Efficient inference in fully connected crfs with gaussian edge potentials. Advances in neural information processing systems 24 (2011)
  * [16] Lee, K.H., Park, C., Oh, J., Kwak, N.: Lfi-cam: learning feature importance for better visual explanation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1355–1363 (2021)
  * [17] Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34(10), 1993–2024 (2014)
  * [18] Omeiza, D., Speakman, S., Cintas, C., Weldermariam, K.: Smooth grad-cam++: An enhanced inference level visualization technique for deep convolutional neural network models. arXiv preprint arXiv:1908.01224 (2019)
  * [19] Pedano, N., Flanders, A.E., Scarpace, L., Mikkelsen, T., Eschbacher, J.M., Hermes, B., Ostrom, Q.: Radiology data from the cancer genome atlas low grade glioma [tcga-lgg] collection. The cancer imaging archive 2 (2016)
  * [20] Qian, Z., Li, K., Lai, M., Chang, E.I.C., Wei, B., Fan, Y., Xu, Y.: Transformer based multiple instance learning for weakly supervised histopathology image segmentation. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part II. pp. 160–170. Springer (2022)
  * [21] Ramaswamy, H.G., et al.: Ablation-cam: Visual explanations for deep convolutional network via gradient-free localization. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 983–991 (2020)
  * [22] Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. pp. 618–626 (2017)
  * [23] Tang, W., Kang, H., Cao, Y., Yu, P., Han, H., Zhang, R., Chen, K.: M-seam-nam: Multi-instance self-supervised equivalent attention mechanism with neighborhood affinity module for double weakly supervised segmentation of covid-19. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 262–272. Springer (2021)
  * [24] Wang, H., Wang, Z., Du, M., Yang, F., Zhang, Z., Ding, S., Mardziel, P., Hu, X.: Score-cam: Score-weighted visual explanations for convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 24–25 (2020)
  * [25] Wang, Y., Zhang, J., Kan, M., Shan, S., Chen, X.: Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12275–12284 (2020)
  * [26] Wen, S.C., Chen, Y.J., Liu, Z., Wen, W., Xu, X., Shi, Y., Ho, T.Y., Jia, Q., Huang, M., Zhuang, J.: Do noises bother human and neural networks in the same way? a medical image analysis perspective. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). pp. 1166–1170. IEEE (2020)
  * [27] Xu, X., Wang, T., Shi, Y., Yuan, H., Jia, Q., Huang, M., Zhuang, J.: Whole heart and great vessel segmentation in congenital heart disease using deep neural networks and graph matching. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 477–485. Springer (2019)
  * [28] Zhang, X., Wei, Y., Feng, J., Yang, Y., Huang, T.S.: Adversarial complementary learning for weakly supervised object localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1325–1334 (2018)
  * [29] Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2921–2929 (2016)