BloodImage: Benchmarking vision transformers for blast detection in digital blood films using public and clinical datasets.

ABSTRACT:

Background and objectives

Leukemia is one of the most common cancers in the UK and it is usually initially diagnosed through the time-consuming and subjective analysis of blood films by an expert hematologist. When a small number of blast cells may be present on a blood film, it is difficult to detect them even after a thorough review. Automating blood film image analysis could significantly speed up the process and improve diagnostic accuracy. This study benchmarks a machine learning framework based on vision transformers (ViTs) for automated blast detection in digitized blood films, evaluating their generalizability across public and clinical datasets.

Methods

We investigated different training strategies (hold-out/k-fold cross-validation), optimization (Adam or stochastic

SUBMITTER: Piazzese C 

PROVIDER: S-EPMC12719678 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature