In urban cities, visual information on and along roadways is likely to distract drivers and lead to missing traffic signs and other accident-prone (AP) features. To avoid accidents due to missing these visual cues, this paper proposes a visual notification of AP-features to drivers based on real-time images obtained via dashcam. For this purpose, Google Street View images around accident hotspots (areas of dense accident occurrence) identified by a real-accident dataset are used to train a novel attention module to classify a given urban scene into an accident hotspot or a non-hotspot (area of sparse accident occurrence). The proposed module leverages channel, point, and spatial-wise attention learning on top of different CNN backbones. This leads to better classification results and more certain AP-features with better contextual knowledge when compared with CNN backbones alone. Our proposed module achieves up to 92% classification accuracy. The capability of detecting AP-features by the proposed model were analyzed by a comparative study of three different class activation map (CAM) methods, which were used to inspect specific AP-features causing the classification decision. Outputs of CAM methods were processed by an image processing pipeline to extract only the AP-features that are explainable to drivers and notified using a visual notification system. Range of experiments was performed to prove the efficacy and AP-features of the system. Ablation of the AP-features taking 9.61%, on average, of the total area in each image increased the chance of a given area to be classified as a non-hotspot by up to 21.8%.

/pdf/sensing-accident-prone-features-in-urban-scenes-for-19ixl7nx.pdf

Sensing accident-prone features in urban scenes for proactive driving and accident prevention

Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which is critical for patient evaluation and treatment planning. To reduce the labor and expertise required for labeling, weakly-supervised semantic segmentation (WSSS) methods with class activation mapping (CAM) have been proposed. However, existing CAM methods suffer from low resolution due to strided convolution and pooling layers, resulting in inaccurate predictions. In this study, we propose a novel CAM method, Attentive Multiple-Exit CAM (AME-CAM), that extracts activation maps from multiple resolutions to hierarchically aggregate and improve prediction accuracy. We evaluate our method on the BraTS 2021 dataset and show that it outperforms state-of-the-art methods.

AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain Tumor

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.

A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation

Class Activation Mapping (CAM) can be used to obtain a visual understanding of the predictions made by Convolutional Neural Networks (CNNs), facilitating qualitative insight into these neural networks when they are, for instance, used for the purpose of medical image analysis. In this paper, we investigate to what extent CAM also enables a quantitative understanding of CNN-based classification models through the creation of segmentation masks out of class activation maps, hereby targeting the use case of brain tumor classification. To that end, when a class activation map has been created for a correctly classified brain tumor, we additionally perform tumor segmentation by binarization of the aforementioned map, leveraging different methods for thresholding. In a next step, we compare this CAM-based segmentation mask to the segmentation ground truth, measuring similarity through the use of Intersection over Union (IoU). Our experimental results show that, although our CNN-based classification models have a similarly high accuracy between 86.0% and 90.8%, their generated masks are different. For example, our Modified VGG-16 model scores an mIoU of 12.2%, whereas AlexNet scores an mIoU of 2.1%. When comparing with the mIoU obtained by our U-Net-based models, which is between 66.6% and 67.3%, and where U-Net is a dedicated pixel-wise segmentation model, our experimental results point to a significant difference in terms of segmentation effectiveness. As such, the use of CAM for the purpose of proxy segmentation or as a ground truth segmentation mask generator comes with several limitations.

/pdf/towards-a-quantitative-analysis-of-class-activation-mapping-4ew5bo3at4.pdf

Yuju Ahn

Papers

Towards a Quantitative Analysis of Class Activation Mapping for Deep Learning-based Computer-Aided Diagnosis