Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia Classification

arXiv:2403.05379v1 Announce Type: new
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, …

abstract aml analysis annotations arxiv automated challenges classification cs.ai cs.cv datasets deep learning diagnosis disease disease diagnosis diseases image instance medical mil multiple training type