From Data to Diagnosis: A Large, Comprehensive Bone Marrow Dataset and AI Methods for Childhood Leukemia Prediction

Leukemia diagnosis primarily relies on manual microscopic analysis of bone marrow morphology supported by additional laboratory parameters, making it complex and time consuming. While artificial intelligence (AI) solutions have been proposed, most utilize private datasets and only cover parts of the diagnostic pipeline. Therefore, we present a large, high-quality, publicly available leukemia bone marrow dataset spanning the entire diagnostic process, from cell detection to diagnosis. Using this dataset, we further propose methods for cell detection, cell classification, and diagnosis prediction. The dataset comprises 246 pediatric patients with diagnostic, clinical and laboratory information, over 40 000 cells with bounding box annotations and more than 28 000 of these with high-quality class labels, making it the most comprehensive dataset publicly available. Evaluation of the AI models yielded an average precision of 0.96 for the cell detection, an area under the curve of 0.98, and an F1-score of 0.61 for the 33-class cell classification, and a mean F1-score of 0.90 for the diagnosis prediction using predicted cell counts. While the proposed approaches demonstrate their usefulness for AI-assisted diagnostics, the dataset will foster further research and development in the field, ultimately contributing to more precise diagnoses and improved patient outcomes.

Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model

Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more heterogeneous datasets common in biomedical imaging. Here, we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a Universal bioMedical PreTrained model (UMedPT) on a multi-task database including tomographic, microscopic, and X-ray images, with various labelling strategies such as classification, segmentation, and object detection. The UMedPT foundational model outperformed ImageNet pretraining and the previous state-of-the-art models. For tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required not more than 50% of the original training data. In an external independent validation imaging features extracted using UMedPT proved to be a new standard for cross-center transferability.

The NCI Imaging Data Commons as a platform for reproducible research in computational pathology

Objective: Reproducibility is critical for translating machine learning-based (ML) solutions in computational pathology (CompPath) into practice. However, an increasing number of studies report difficulties in reproducing ML results. The NCI Imaging Data Commons (IDC) is a public repository of >120 cancer image collections, including >38,000 whole-slide images (WSIs), that is designed to be used with cloud-based ML services. Here, we explore the potential of the IDC to facilitate reproducibility of CompPath research. Materials and Methods: The IDC realizes the FAIR principles: All images are encoded according to the DICOM standard, persistently identified, discoverable via rich metadata, and accessible via open tools. Taking advantage of this, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets from the IDC. To assess reproducibility, the experiments were run multiple times with independent but identically configured sessions of common ML services. Results: The AUC values of different runs of the same experiment were generally consistent and in the same order of magnitude as a similar, previously published study. However, there were occasional small variations in AUC values of up to 0.044, indicating a practical limit to reproducibility. Discussion and conclusion: By realizing the FAIR principles, the IDC enables other researchers to reuse exactly the same datasets. Cloud-based ML services enable others to run CompPath experiments in an identically configured computing environment without having to own high-performance hardware. The combination of both makes it possible to approach the reproducibility limit.