Summary of Going Beyond H&e and Oncology: How Do Histopathology Foundation Models Perform For Multi-stain Ihc and Immunology?, by Amaya Gallagher-syed et al.
Going Beyond H&E and Oncology: How Do Histopathology Foundation Models Perform for Multi-stain IHC and Immunology?
by Amaya Gallagher-Syed, Elena Pontarini, Myles J. Lewis, Michael R. Barnes, Gregory Slabaugh
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM); Tissues and Organs (q-bio.TO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study compares the generalization capabilities of state-of-the-art histopathology foundation models on out-of-distribution multi-stain autoimmune Immunohistochemistry datasets, evaluating 13 feature extractor models including ImageNet-pretrained networks and histopathology foundation models trained on both public and proprietary data. The models are tested on Rheumatoid Arthritis subtyping and Sjogren’s Disease detection tasks using an Attention-Based Multiple Instance Learning classifier to assess the transferability of learned representations from cancer H&E images to autoimmune IHC images. The results show that histopathology-pretrained models do not significantly outperform ImageNet-pretrained models, highlighting challenges in transferring knowledge from cancer to autoimmune histopathology and emphasizing the need for careful evaluation of AI models across diverse histopathological tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study explores how well AI models can work on new, unseen data. It looks at 13 different types of AI models that are used in medical imaging and compares their performance on two specific tasks: identifying Rheumatoid Arthritis and detecting Sjogren’s Disease. The results show that the best-performing models were those trained on a mix of public and private datasets. This research is important because it helps us understand how well AI models can generalize, or work on new data without being specifically trained for it. |
Keywords
» Artificial intelligence » Attention » Generalization » Transferability
