Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia Classification

• URL: http://arxiv.org/abs/2403.05379v2
• Date: Thu, 22 Aug 2024 21:42:24 GMT
• Title: Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia Classification
• Authors: Salome Kazeminia, Max Joosten, Dragan Bosnacki, Carsten Marr,
• Abstract summary: Diseases like Acute Myeloid Leukemia (AML) pose challenges due to scarce and costly annotations on a single-cell level. Multiple Instance Learning (MIL) addresses weakly labeled scenarios but necessitates powerful encoders typically trained with labeled data. In this study, we explore Self-Supervised Learning (SSL) as a pre-training approach for MIL-based subtype AML classification from blood smears.
• Score: 1.1874560263468232
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automated disease diagnosis using medical image analysis relies on deep learning, often requiring large labeled datasets for supervised model training. Diseases like Acute Myeloid Leukemia (AML) pose challenges due to scarce and costly annotations on a single-cell level. Multiple Instance Learning (MIL) addresses weakly labeled scenarios but necessitates powerful encoders typically trained with labeled data. In this study, we explore Self-Supervised Learning (SSL) as a pre-training approach for MIL-based AML subtype classification from blood smears, removing the need for labeled data during encoder training. We investigate the three state-of-the-art SSL methods SimCLR, SwAV, and DINO, and compare their performance against supervised pre-training. Our findings show that SSL-pretrained encoders achieve comparable performance, showcasing the potential of SSL in MIL. This breakthrough offers a cost-effective and data-efficient solution, propelling the field of AI-based disease diagnosis.

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