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Found 5 papers, 3 papers with code
On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical Imaging
Deep neural networks excel in medical imaging but remain prone to biases, leading to fairness gaps across demographic groups.
ACE: Anatomically Consistent Embeddings in Composition and Decomposition
Medical images acquired from standardized protocols show consistent macroscopic or microscopic anatomical structures, and these structures consist of composable/decomposable organs and tissues, but existing self-supervised learning (SSL) methods do not appreciate such composable/decomposable structure attributes inherent to medical images.
DWARF: Disease-weighted network for attention map refinement
The interpretability of deep learning is crucial for evaluating the reliability of medical imaging models and reducing the risks of inaccurate patient recommendations.
DeViDe: Faceted medical knowledge for improved medical vision-language pre-training
These descriptions outline general visual characteristics of diseases in radiographs, and when combined with abstract definitions and radiology reports, provide a holistic snapshot of knowledge.
Learning Anatomically Consistent Embedding for Chest Radiography
Self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated images.
