Abstract
Whole slide image (WSI) classification is a critical task in computational pathology, requiring the processing of gigapixel-sized images, which is challenging for current deep-learning methods. Current state of the art methods are based on multi-instance learning schemes (MIL), which usually rely on pretrained features to represent the instances. Due to the lack of task-specific annotated data, these features are either obtained from well-established backbones on natural images, or, more recently from self-supervised models pretrained on histopathology. However, both approaches yield task-agnostic features, resulting in performance loss compared to the appropriate task-related supervision, if available. In this paper, we show that when task-specific annotations are limited, we can inject such supervision into downstream task training, to reduce the gap between fully task-tuned and task agnostic features. We propose Prompt-MIL, an MIL framework that integrates prompts into WSI classification. Prompt-MIL adopts a prompt tuning mechanism, where only a small fraction of parameters calibrates the pretrained features to encode task-specific information, rather than the conventional full fine-tuning approaches. Extensive experiments on three WSI datasets, TCGA-BRCA, TCGA-CRC, and BRIGHT, demonstrate the superiority of Prompt-MIL over conventional MIL methods, achieving a relative improvement of 1.49%–4.03% in accuracy and 0.25%–8.97% in AUROC while using fewer than 0.3% additional parameters. Compared to conventional full fine-tuning approaches, we fine-tune less than 1.3% of the parameters, yet achieve a relative improvement of 1.29%–13.61% in accuracy and 3.22%–27.18% in AUROC and reduce GPU memory consumption by 38%–45% while training 21%–27% faster.
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Acknowledgements
This work was partially supported by the ANR Hagnodice ANR-21-CE45-0007, the NSF IIS-2212046, the NSF IIS-2123920, the NIH 1R21CA258493-01A1, the NCI UH3CA225021 and Stony Brook University Provost Funds. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Zhang, J. et al. (2023). Prompt-MIL: Boosting Multi-instance Learning Schemes via Task-Specific Prompt Tuning. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. https://doi.org/10.1007/978-3-031-43993-3_60
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