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

arXiv: https://arxiv.org/abs/2306.14505 · HTML: https://mlsanity.com/paper/2306.14505/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 26 Jun 2023.

## Builds on (most cited first, 18 of 18)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2004.04581/) (2020)
- [Dilated Residual Networks](https://mlsanity.com/paper/1705.09914/) (2017)
- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [Optimized U-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2110.03352/) (2021)
- [A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains](https://mlsanity.com/paper/1912.11186/) (2019)
- [F-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling](https://mlsanity.com/paper/2109.07069/) (2021)
- [Transformer based multiple instance learning for weakly supervised histopathology image segmentation](https://mlsanity.com/paper/2205.08878/) (2022)
- [ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2103.03664/) (2021)
- [LFI-CAM: Learning Feature Importance for Better Visual Explanation](https://mlsanity.com/paper/2105.00937/) (2021)
- [Poly-CAM: High resolution class activation map for convolutional neural networks](https://mlsanity.com/paper/2204.13359/) (2022)
- [Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation](https://mlsanity.com/paper/2209.01604/) (2022)
- [Medical Image Segmentation via Unsupervised Convolutional Neural Network](https://mlsanity.com/paper/2001.10155/) (2020)
- [Do Noises Bother Human and Neural Networks In the Same Way? A Medical Image Analysis Perspective](https://mlsanity.com/paper/2011.02155/) (2020)
- ["One-Shot" Reduction of Additive Artifacts in Medical Images](https://mlsanity.com/paper/2110.12274/) (2021)

## Built on by (most cited first, 8 of 8)

- [Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning](https://mlsanity.com/paper/2403.02566/) (2024)
- [ToNNO: Tomographic Reconstruction of a Neural Network's Output for Weakly Supervised Segmentation of 3D Medical Images](https://mlsanity.com/paper/2404.13103/) (2024)
- [Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentation](https://mlsanity.com/paper/2603.08605/) (2026)
- [Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature Masking](https://mlsanity.com/paper/2505.08604/) (2025)
- [Contrastive Learning with Diffusion Features for Weakly Supervised Medical Image Segmentation](https://mlsanity.com/paper/2506.23460/) (2025)
- [RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scans](https://mlsanity.com/paper/2510.08052/) (2025)
- [Hybrid Transformer-Mamba for Weakly Supervised Volumetric Medical Segmentation](https://mlsanity.com/paper/2512.10353/) (2025)
- [MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities](https://mlsanity.com/paper/2608.19788/) (2026)

## Abstract (arXiv)

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.