INSIGHT: Explainable Weakly-Supervised Medical Image Analysis
Due to their large sizes, volumetric scans and whole-slide pathology images (WSIs) are often processed by extracting embeddings from local regions and then an aggregator makes predictions from this set. However, current methods require post-hoc visualization techniques (e.g., Grad-CAM) and often fail to localize small yet clinically crucial details. To address these limitations, we introduce INSIGHT, a novel weakly-supervised aggregator that integrates heatmap generation as an inductive bias. Starting from pre-trained feature maps, INSIGHT employs a detection module with small convolutional kernels to capture fine details and a context module with a broader receptive field to suppress local false positives. The resulting internal heatmap highlights diagnostically relevant regions. On CT and WSI benchmarks, INSIGHT achieves state-of-the-art classification results and high weakly-labeled semantic segmentation performance. Project website and code are available at: https://zhangdylan83.github.io/ewsmia/
Code (0)
등록된 구현이 없습니다.
Tasks
Inductive BiasMedical Image AnalysisSemantic SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
TWLR: Text-Guided Weakly-Supervised Lesion Localization and Severity Regression for Explainable Diabetic Retinopathy Grading
Accurate medical image analysis can greatly assist clinical diagnosis, but its effectiveness relies on high-quality expert annotations Obtaining pixel-level labels for medical images, particularly fundus images, remains …
Weakly-Supervised Semantic SegmentationDiabetic Retinopathy GradingLesion SegmentationWeakly and Semi Supervised Detection in Medical Imaging via Deep Dual Branch Net
This study presents a novel deep learning architecture for multi-class classification and localization of abnormalities in medical imaging illustrated through experiments on mammograms. The proposed network combines two …
ClassificationGeneral ClassificationMulti-class ClassificationWeakly-supervised LearningDenoising Diffusion Models for Anomaly Localization in Medical Images
This chapter explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their application to image reconstruction and th…
Anomaly LocalizationDenoisingImage ReconstructionWeakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images
Deep Convolutional Neural Networks have proven effective in solving the task of semantic segmentation. However, their efficiency heavily relies on the pixel-level annotations that are expensive to get and often require d…
Relation NetworkSegmentationSemantic SegmentationWeakly supervised segmentation+2CaCL: Class-aware Codebook Learning for Weakly Supervised Segmentation on Diffuse Image Patterns
Weakly supervised learning has been rapidly advanced in biomedical image analysis to achieve pixel-wise labels (segmentation) from image-wise annotations (classification), as biomedical images naturally contain image-wis…
Image ReconstructionSegmentationWeakly-supervised LearningWeakly supervised segmentation