Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia
  Classification
Salome Kazeminia Max Joosten Dragan Bosnacki Carsten Marr
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.