Snuffy: Efficient Whole Slide Image Classifier
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917941503590400 |
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| author | Jafarinia, Hossein Alipanah, Alireza Hamdi, Danial Razavi, Saeed Mirzaie, Nahal Rohban, Mohammad Hossein |
| author_facet | Jafarinia, Hossein Alipanah, Alireza Hamdi, Danial Razavi, Saeed Mirzaie, Nahal Rohban, Mohammad Hossein |
| contents | Whole Slide Image (WSI) classification with multiple instance learning (MIL) in digital pathology faces significant computational challenges. Current methods mostly rely on extensive self-supervised learning (SSL) for satisfactory performance, requiring long training periods and considerable computational resources. At the same time, no pre-training affects performance due to domain shifts from natural images to WSIs. We introduce Snuffy architecture, a novel MIL-pooling method based on sparse transformers that mitigates performance loss with limited pre-training and enables continual few-shot pre-training as a competitive option. Our sparsity pattern is tailored for pathology and is theoretically proven to be a universal approximator with the tightest probabilistic sharp bound on the number of layers for sparse transformers, to date. We demonstrate Snuffy's effectiveness on CAMELYON16 and TCGA Lung cancer datasets, achieving superior WSI and patch-level accuracies. The code is available on https://github.com/jafarinia/snuffy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08258 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Snuffy: Efficient Whole Slide Image Classifier Jafarinia, Hossein Alipanah, Alireza Hamdi, Danial Razavi, Saeed Mirzaie, Nahal Rohban, Mohammad Hossein Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Neural and Evolutionary Computing Image and Video Processing Whole Slide Image (WSI) classification with multiple instance learning (MIL) in digital pathology faces significant computational challenges. Current methods mostly rely on extensive self-supervised learning (SSL) for satisfactory performance, requiring long training periods and considerable computational resources. At the same time, no pre-training affects performance due to domain shifts from natural images to WSIs. We introduce Snuffy architecture, a novel MIL-pooling method based on sparse transformers that mitigates performance loss with limited pre-training and enables continual few-shot pre-training as a competitive option. Our sparsity pattern is tailored for pathology and is theoretically proven to be a universal approximator with the tightest probabilistic sharp bound on the number of layers for sparse transformers, to date. We demonstrate Snuffy's effectiveness on CAMELYON16 and TCGA Lung cancer datasets, achieving superior WSI and patch-level accuracies. The code is available on https://github.com/jafarinia/snuffy. |
| title | Snuffy: Efficient Whole Slide Image Classifier |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Neural and Evolutionary Computing Image and Video Processing |
| url | https://arxiv.org/abs/2408.08258 |