Papers for

clinical diagnostic teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Hierarchical model improves bone marrow cell classification and maturity ordering

HemaHier: Chain-Conditioned Ordinal Hierarchies for Lineage-Aware Bone-Marrow Cytology

Abstract: Bone-marrow cytology is inherently structured: each cell belongs to a hematopoietic lineage, and many cell types lie on ordered maturation trajectories. Standard flat classifiers ignore this structure, treating a mild same-lineage confusion the same as a severe cross-lineage mistake and predicting only discrete labels. We propose HemaHier, an ordinal-hierarchical prediction head for a frozen or lightly adapted cytology foundation model. Its central component is a chain-conditioned maturity score that reads a single maturity value under a per-chain query, supervised only on biologically valid healthy chains, while dysplastic and off-chain cell types remain classes but are excluded from maturity supervision. Fine and lineage predictions are coupled through a shared posterior that guarantees hierarchical consistency, and a staged objective first stabilizes recognition, then adds lineage and maturity supervision. On three bone-marrow datasets under a shared ontology, HemaHier achieves competitive recognition while reducing biologically severe errors and adding a within-lineage maturity ordering that flat classifiers lack. Code is available at https://github.com/xmindflow/HemaHier.

Fri 11 SeptComputer Vision and Pattern Recognition
The gist
Classifying bone marrow cells is tricky because cells belong to families and mature in order. The authors designed a model called HemaHier that respects these family trees and maturity stages, unlike common models that treat all mistakes equally. HemaHier reduces serious errors and adds maturity ordering inside cell families, helping better identify healthy and abnormal cells. They tested it on three bone marrow datasets and shared the code for others to use.
Open → 2609.12835v1

Cardiac imaging data improves ECG detection of Chagas disease

Leveraging Cardiac Imaging to Improve ECG-Based Detection of Chagas Disease in Resource-Constrained Settings

Abstract: Chagas disease is a major cause of cardiomyopathy in Latin America. Cardiac magnetic resonance (CMR) imaging can characterize its structural abnormalities, but scanners and expert readers remain scarce in endemic regions. Electrocardiography (ECG) is inexpensive and widely available, yet structural disease must be inferred indirectly from electrical signals. We propose to transfer CMR-derived structural knowledge to ECG through contrastive pre-training. Using 63,193 paired ECG-CMR examinations from the UK Biobank, we align an ECG encoder with a clinically grounded CMR embedding space using an asymmetric InfoNCE objective. Despite seeing no Chagas cases during pre-training, the resulting representation improves ECG-based Chagas detection. Across CODE-15% and SaMi-Trop, a frozen linear probe achieves an AUROC of 0.851 and sensitivity at the top 5% of predicted risk (Top5%-TPR) of 0.427 in five-fold cross-validation, compared with 0.827 and 0.377 for an unaligned ECG-FM baseline. On the PhysioNet/CinC 2025 Challenge test set, our model obtains the highest AUROC on SaMi-Trop-3 and the best ELSA-Brasil challenge score among the three top-performing methods, indicating that imaging-supervised ECG representations can generalize to populations and settings beyond the pre-training distribution.

Tue 8 SeptMachine LearningArtificial Intelligence
The gist
Chagas disease affects the heart and is common in Latin America. Although detailed heart scans called CMR can show damage clearly, these scans and experts are rare in places where the disease is common. ECGs, which measure the heart’s electrical activity, are easier to get but less direct for detecting damage. The authors trained a computer model to learn from the better heart scans and then used that knowledge to improve disease detection from ECGs alone. This method worked well even in different countries and with patients not seen before.
Open → 2609.08582v1