Predicting Genetic Mutations from Single-Cell Bone Marrow Images in Acute Myeloid Leukemia Using Noise-Robust Deep Learning Models

• URL: http://arxiv.org/abs/2506.12798v1
• Date: Sun, 15 Jun 2025 10:15:42 GMT
• Title: Predicting Genetic Mutations from Single-Cell Bone Marrow Images in Acute Myeloid Leukemia Using Noise-Robust Deep Learning Models
• Authors: Garima Jain, Ravi Kant Gupta, Priyansh Jain, Abhijeet Patil, Ardhendu Sekhar, Gajendra Smeeta, Sanghamitra Pati, Amit Sethi,
• Abstract summary: We propose a robust methodology for identification of myeloid blasts followed by prediction of genetic mutation in single-cell images of blasts.<n>We trained an initial binary classifier to distinguish between leukemic (blasts) and non-leukemic cells images, achieving 90 percent accuracy.<n>Despite the tumor label noise, our mutation classification model achieved 85 percent accuracy across four mutation classes, demonstrating resilience to label inconsistencies.
• Score: 2.6995203611040455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we propose a robust methodology for identification of myeloid blasts followed by prediction of genetic mutation in single-cell images of blasts, tackling challenges associated with label accuracy and data noise. We trained an initial binary classifier to distinguish between leukemic (blasts) and non-leukemic cells images, achieving 90 percent accuracy. To evaluate the models generalization, we applied this model to a separate large unlabeled dataset and validated the predictions with two haemato-pathologists, finding an approximate error rate of 20 percent in the leukemic and non-leukemic labels. Assuming this level of label noise, we further trained a four-class model on images predicted as blasts to classify specific mutations. The mutation labels were known for only a bag of cell images extracted from a single slide. Despite the tumor label noise, our mutation classification model achieved 85 percent accuracy across four mutation classes, demonstrating resilience to label inconsistencies. This study highlights the capability of machine learning models to work with noisy labels effectively while providing accurate, clinically relevant mutation predictions, which is promising for diagnostic applications in areas such as haemato-pathology.

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