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Prediction of B/T Subtype, and ETV6-RUNX1 Translocation in Pediatric Acute Lymphoblastic Leukemia By Analysis of Giemsa-Stained Whole Slide Images

Arkadi Piven

Supervised by Gil Shamai, Roy Velich


In this project, we analyze digitized Giemsa-stained bone marrow samples using deep learning techniques to predict B/T subtype and ETV6-RUNX1 translocation in Acute Lymphoblastic Leukemia (ALL). We developed a solution to predict patient-specific medical properties from digitized slides in a statistically significant manner, employing a convolutional neural network (CNN) trained through supervised learning on labeled medical data provided by multiple institutions. Moreover, we experimented with attention-based methods to further improve our results. Our results show that it is possible to predict these medical characteristics using a CNN-based method, but we need more data to further exploit the attention-based method.

Project Report

Please, see project report.

Final Presentation

Please, see final presentation.

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