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Person-Based Progression in Acute Myeloid Leukemia Classification: A Multi-View Convolutional Neural Network Approach

Acute myeloid leukemia (AML) is a hematologic malignancy in which accurate subtype identification is important for treatment planning and prognosis. This study developed a deep learning approach for AML subtype classification using multiple single-cell images from each patient rather than conventional instance-based classification. An open-source dataset containing 81,214 single-cell images from 189 individuals was analyzed. The dataset included four genetically defined AML subtypes and a healthy control group.…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1264· cite & verify
Person-Based Progression in Acute Myeloid Leukemia Classification: A Multi-View Convolutional Neural Network Approach
Canine piroplasmosis Plate IX Nuttall 1905 14 15 by Nuttall, George Henry Falkiner, 1862-1937. Public domain

In brief

By the publication date of 2026-10-01, researchers had developed and evaluated a person-based multi-view CNN approach for AML subtype classification using 81,214 single-cell images from 189 individuals. The 500-view MV-CNN achieved accuracy 0.8783, F1 0.8622, sensitivity 0.8637, specificity 0.8774, precision 0.8616 and MCC 0.8441, outperforming a single-view CNN baseline in tenfold cross-validation.

The result matters because accurate subtype identification is important for treatment planning and prognosis in AML, and patient-level aggregation of multiple cell images improved classification over conventional instance-based analysis. What remains uncertain is clinical translation, as the source describes performance on an open-source dataset and supports potential for hematologic image classification rather than reporting prospective clinical deployment or patient outcomes.

Main points

  1. Study used open-source dataset of 81,214 single-cell images from 189 individuals covering four genetically defined AML subtypes and healthy controls.
  2. Two MV-CNN models were tested: 99-view model based on random sampling and 500-view model using image augmentation when fewer than 500 images were available per patient.
  3. Performance assessed by tenfold cross-validation on accuracy, F1, sensitivity, specificity, precision, and MCC; single-view CNN yielded the lowest values.

The gain

Patient-level 500-view convolutional neural network improved acute myeloid leukemia subtype classification, achieving 0.8783 accuracy and 0.8622 F1 score compared to single-view analysis.

The rundown

The comparison was a single-view CNN versus two multi-view approaches, with the 99-view model showing lower but comparable performance to the 500-view model.

Dataset included four genetically defined AML subtypes plus a healthy control group, and evaluation used tenfold cross-validation across six metrics including Matthews correlation coefficient.

Sources

  1. Peer-reviewedJournal of Imaging Informatics in Medicine2026-10-01

The debate