arXiv Computer Vision
Measuring What Matters: A Causal Reliance Score for Histopathology Image Segmentation
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arXiv AI
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To do($x$) or not to do($x$): Medical Image Counterfactuals for Dataset Augmentation
arXiv:2609.14124v1 Announce Type: cross Abstract: Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy...
arXiv Machine Learning
Aug 5
Assessment of Conditional Diffusion Model for Synthetic Histopathology Image Generation
arXiv:2608. 03990v1 Announce Type: new Abstract: Synthetic histopathology image generation has emerged as an approach that may address data scarcity in computational pathology, yet current evaluation methodologies may not fully assess synthetic data quality for medical applications.
arXiv AI
Jul 2
Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation
arXiv:2601. 08127v2 Announce Type: replace-cross Abstract: Expert-annotated training data remains the critical bottleneck for AI in histopathology, particularly for rare pathologies where even dozens of cases may be unavailable.
arXiv Computer Vision
Oct 7
Scalable next-scale autoregression for medical image generation across anatomical regions
arXiv:2602.14512v3 Announce Type: replace Abstract: Autoregressive pretraining has been key to the scalability of large language models, yet medical generative foundation models remain predominantly...
arXiv Computer Vision
Sep 1
Learning To Focus: Anatomy-Guided Attention Regularization for Medical Image Classification
arXiv:2607.10851v2 Announce Type: replace Abstract: Medical image classification models are ideally expected to identify diagnostically relevant regions while making predictions, yet standard classif...
arXiv Computer Vision
Sep 3
Generating Medical Image Counterfactuals using Causal Explanations
The paper introduces a new framework for generating medical image counterfactuals that does not rely on auxiliary generative models. By extracting causal evidence directly from a classifier, the method deterministically produces edits within user-specified regions, requiring no additional training. Experiments on real-world medical imaging datasets show that these counterfactuals alter classifier predictions while staying closer to the original image than generative baselines, offering a clearer view of the model’s decision boundary.