Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles
Open accessAbstract
We present ARCH, a computational pathology (CP) multiple instance captioning dataset to facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks; ARCH on the other hand contains dense diagnos-tic and morphological descriptions for a range of stains, tissue types and pathologies. Using intrinsic dimensionality estimation, we show that ARCH is the only CP dataset to (ARCH-)rival its computer vision analog MS-COCO Captions. We conjecture that an encoder pre-trained on dense image captions learns transferable representations for most CP tasks. We support the conjecture with evidence that ARCH representation transfers to a variety of pathology sub-tasks better than ImageNet features or representations obtained via self-supervised or multi-task learning on pathology images alone. We release our best model and invite other researchers to test it on their CP tasks.
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Of the three generative SSL studies, two reported comparisons with purely supervised models and showed relative improvements of 16.6% and 24.5% with self-supervised learning.
“Of the three studies, two reported comparisons with purely supervised models and showed relative improvements of 16.6%58 and 24.5%56 with self-supervised learning.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [56]
The remaining studies explored multiple other tasks and many focused on classification of malignant disorders such as breast cancer, skin cancer, and lung cancer.
“The remaining studies explored multiple other tasks and many focused on classification of malignant disorders such as breast cancer56,61,88, skin cancer89, and lung cancer59.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [56]
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- Authors
- Gamper, J., & Rajpoot, N.
- Affiliations
- University of Warwick (GB)
- ORCID
- Rajpoot, Nasir
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 16544-16554
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3135141466
- Cited by
- 75
- References
- 58
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 30 · 2023: 16 · 2022: 5 · 2021: 2
- FWCI
- 6.12
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- computational pathology, histopathology, representation transfer, intrinsic dimensionality estimation, histopathological image classification, morphological description
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