The briefing
Researchers present a segmentation-guided framework combining HemaNest-UNet++ and an explainable vision transformer to detect ALL and classify subtypes from bone marrow microscopy. Tested on 387 labeled images from a single laboratory, the system achieved high accuracy with visual explanations highlighting clinically relevant features.
Original headline
HemaNest-UNet++ and explainable vision transformer for acute lymphoblastic leukemia classification