Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
Contrastive visual language pretraining has emerged as a powerful method for either training new language-aware image encoders or augmenting existing pretrained models with zero-shot visual recognition capabilities. However, existing works typically train on large datasets of image-text pairs and ha...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/151651 |
| _version_ | 1826190992734158848 |
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| author | Lu, Ming Yang (Max) |
| author2 | Mahmood, Faisal |
| author_facet | Mahmood, Faisal Lu, Ming Yang (Max) |
| author_sort | Lu, Ming Yang (Max) |
| collection | MIT |
| description | Contrastive visual language pretraining has emerged as a powerful method for either training new language-aware image encoders or augmenting existing pretrained models with zero-shot visual recognition capabilities. However, existing works typically train on large datasets of image-text pairs and have been designed to perform downstream tasks involving only small to medium sized-images, neither of which are applicable to the emerging field of computational pathology where there are limited publicly available paired image-text datasets and each image can span up to 100,000 x 100,000 pixels. In this paper we present MI-Zero, a simple and intuitive framework for unleashing the zero-shot transfer capabilities of contrastively aligned image and text models on gigapixel histopathology whole slide images, enabling multiple downstream diagnostic tasks to be carried out by pretrained encoders without requiring any additional labels. MI-Zero reformulates zero-shot transfer under the framework of multiple instance learning to overcome the computational challenge of inference on extremely large images. We used over 550k pathology reports and other available in-domain text corpora to pretrain our text encoder. By effectively leveraging strong pretrained encoders, our best model pretrained on over 33k histopathology image-caption pairs achieves an average median zero-shot accuracy of 70.2% across three different real-world cancer subtyping tasks. |
| first_indexed | 2024-09-23T08:48:44Z |
| format | Thesis |
| id | mit-1721.1/151651 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:48:44Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1516512023-08-01T04:16:57Z Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images Lu, Ming Yang (Max) Mahmood, Faisal Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Contrastive visual language pretraining has emerged as a powerful method for either training new language-aware image encoders or augmenting existing pretrained models with zero-shot visual recognition capabilities. However, existing works typically train on large datasets of image-text pairs and have been designed to perform downstream tasks involving only small to medium sized-images, neither of which are applicable to the emerging field of computational pathology where there are limited publicly available paired image-text datasets and each image can span up to 100,000 x 100,000 pixels. In this paper we present MI-Zero, a simple and intuitive framework for unleashing the zero-shot transfer capabilities of contrastively aligned image and text models on gigapixel histopathology whole slide images, enabling multiple downstream diagnostic tasks to be carried out by pretrained encoders without requiring any additional labels. MI-Zero reformulates zero-shot transfer under the framework of multiple instance learning to overcome the computational challenge of inference on extremely large images. We used over 550k pathology reports and other available in-domain text corpora to pretrain our text encoder. By effectively leveraging strong pretrained encoders, our best model pretrained on over 33k histopathology image-caption pairs achieves an average median zero-shot accuracy of 70.2% across three different real-world cancer subtyping tasks. S.M. 2023-07-31T19:56:11Z 2023-07-31T19:56:11Z 2023-06 2023-07-13T14:23:06.551Z Thesis https://hdl.handle.net/1721.1/151651 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Lu, Ming Yang (Max) Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images |
| title | Visual Language Pretrained Multiple Instance Zero-Shot
Transfer for Histopathology Images |
| title_full | Visual Language Pretrained Multiple Instance Zero-Shot
Transfer for Histopathology Images |
| title_fullStr | Visual Language Pretrained Multiple Instance Zero-Shot
Transfer for Histopathology Images |
| title_full_unstemmed | Visual Language Pretrained Multiple Instance Zero-Shot
Transfer for Histopathology Images |
| title_short | Visual Language Pretrained Multiple Instance Zero-Shot
Transfer for Histopathology Images |
| title_sort | visual language pretrained multiple instance zero shot transfer for histopathology images |
| url | https://hdl.handle.net/1721.1/151651 |
| work_keys_str_mv | AT lumingyangmax visuallanguagepretrainedmultipleinstancezeroshottransferforhistopathologyimages |