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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2211.14312 |
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| _version_ | 1866912284333309952 |
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| author | Shamsi, Zahra Reid, Isaac Bryant, Drew Wilson, Jacob Qu, Xiaoyu Dubey, Avinava Kothari, Konik Dehghani, Mostafa Chavarha, Mariya Likhosherstov, Valerii Williams, Brian Frumkin, Michael Appelbaum, Fred Choromanski, Krzysztof Bashir, Ali Fang, Min |
| author_facet | Shamsi, Zahra Reid, Isaac Bryant, Drew Wilson, Jacob Qu, Xiaoyu Dubey, Avinava Kothari, Konik Dehghani, Mostafa Chavarha, Mariya Likhosherstov, Valerii Williams, Brian Frumkin, Michael Appelbaum, Fred Choromanski, Krzysztof Bashir, Ali Fang, Min |
| contents | We present a machine learning method capable of accurately detecting chromosome abnormalities that cause blood cancers directly from microscope images of the metaphase stage of cell division. The pipeline is built on a series of fine-tuned Vision Transformers. Current state of the art (and standard clinical practice) requires expensive, manual expert analysis, whereas our pipeline takes only 15 seconds per metaphase image. Using a novel pretraining-finetuning strategy to mitigate the challenge of data scarcity, we achieve a high precision-recall score of 94% AUC for the clinically significant del(5q) and t(9;22) anomalies. Our method also unlocks zero-shot detection of rare aberrations based on model latent embeddings. The ability to quickly, accurately, and scalably diagnose genetic abnormalities directly from metaphase images could transform karyotyping practice and improve patient outcomes. We will make code publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_14312 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Karyotype AI for Precision Oncology Shamsi, Zahra Reid, Isaac Bryant, Drew Wilson, Jacob Qu, Xiaoyu Dubey, Avinava Kothari, Konik Dehghani, Mostafa Chavarha, Mariya Likhosherstov, Valerii Williams, Brian Frumkin, Michael Appelbaum, Fred Choromanski, Krzysztof Bashir, Ali Fang, Min Quantitative Methods Computer Vision and Pattern Recognition Machine Learning Image and Video Processing We present a machine learning method capable of accurately detecting chromosome abnormalities that cause blood cancers directly from microscope images of the metaphase stage of cell division. The pipeline is built on a series of fine-tuned Vision Transformers. Current state of the art (and standard clinical practice) requires expensive, manual expert analysis, whereas our pipeline takes only 15 seconds per metaphase image. Using a novel pretraining-finetuning strategy to mitigate the challenge of data scarcity, we achieve a high precision-recall score of 94% AUC for the clinically significant del(5q) and t(9;22) anomalies. Our method also unlocks zero-shot detection of rare aberrations based on model latent embeddings. The ability to quickly, accurately, and scalably diagnose genetic abnormalities directly from metaphase images could transform karyotyping practice and improve patient outcomes. We will make code publicly available. |
| title | Karyotype AI for Precision Oncology |
| topic | Quantitative Methods Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2211.14312 |