Summary of Transformer-based Self-supervised Learning For Histopathological Classification Of Ischemic Stroke Clot Origin, by K. Yeh et al.
Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin
by K. Yeh, M. S. Jabal, V. Gupta, D. F. Kallmes, W. Brinjikji, B. S. Erdal
First submitted to arxiv on: 1 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This study proposes a self-supervised deep learning approach in digital pathology of emboli to classify ischemic stroke clot origin from histopathological images. The approach uses transformer-based models with transfer learning and self-supervised pretraining, along with customizations such as attention pooling layers, weighted loss functions, and threshold optimization. The model is evaluated using a logloss score and achieves a performance of 0.662 in cross-validation and 0.659 on the test set. Different model backbones are compared, with the swin_large_patch4_window12_384 showing higher performance. The study highlights the efficacy of transformer-based deep learning models in identifying ischemic stroke clot origins from histopathological images and emphasizes the need for refined modeling techniques specifically adapted to thrombi WSI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses artificial intelligence to help doctors figure out where blood clots come from in people who have had a stroke. The researchers developed a special kind of computer model that can look at pictures of blood clots taken under a microscope and determine what caused the clot to form. This is important because knowing the cause of the clot can help doctors develop better treatment plans for their patients. The study shows that this AI-powered model is pretty good at doing this job, but there’s still more work to be done to make it even better. |
Keywords
» Artificial intelligence » Attention » Deep learning » Optimization » Pretraining » Self supervised » Transfer learning » Transformer
