ArticleAdvances in neural information processing systems2024
A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis.
Article in Advances in neural information processing systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Review
- CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification.ArXiv · 2026Article
- Cross-domain dynamic routing decoders for multi-domain generalization in ultrasound imaging.Quantitative imaging in medicine and surgery · 2026Article
- Surgical video workflow analysis via visual-language learning.npj health systems · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexpected situations. We investigate this challenge and focus on model sensitivity to domain shifts, such as data sampled from different hospitals or data confounded by demographic variables such as sex, race, etc, in the context of chest X-rays and skin lesion images. A key finding we show empirically is that existing visual backbones lack an appropriate prior from the architecture for reliable generalization in these settings. Taking inspiration from medical training, we propose giving deep networks a prior grounded in explicit medical knowledge communicated in natural language. To this end, we introduce
Identifiers
40352743PMC12064272What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.