arXiv Machine Learning

MS-MLB: An Open Machine Learning Benchmark for Blood-Based MS Classification

arXiv:2608. 05196v1 Announce Type: new Abstract: Multiple sclerosis (MS) is diagnosed through clinical assessment, magnetic resonance imaging, laboratory evidence when appropriate, and exclusion of better explanations.

arXiv AI

Jul 21

Benchmarking Machine Learning Models for Multi-Omics-Based Breast Cancer Prediction

arXiv:2607. 16250v1 Announce Type: cross Abstract: Estrogen Receptor (ER) status is a critical biomarker in breast cancer diagnosis, prognosis, and treatment selection.

By Priyanka Paudel, Madan Baduwal

efficiency benchmarks

arXiv Machine Learning

Jul 7

LeukocyteCount: Automatic Identification and Counting for leukocytes using Deep Learning

arXiv:2607. 04486v1 Announce Type: new Abstract: Diagnosing and monitoring diseases frequently involves the analysis of human biological samples, with blood analysis being pivotal.

By Ahmed M. Sayed (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt), Sondos A. Refaat (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt), Abdallah M. Mostafa (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt), Mariam S. El-Rahmany (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt), Ensaf Hussein Mohamed (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt, School of Information Technology and Computer Science)

arXiv AI

Jul 7

EvoXplain: When Machine Learning Models Agree on Predictions but Disagree on Why -- Measuring Mechanistic Multiplicity Across Training Runs

arXiv:2512. 22240v5 Announce Type: replace-cross Abstract: Machine learning models are primarily judged by predictive performance, especially in applied genomics, where explanations are read as biological findings.

By Chama Bensmail

safety

arXiv AI

Jun 16

Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules

arXiv:2606. 16337v1 Announce Type: new Abstract: Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance but also transparent decision logic.

By Wei Xu, Ke Yang, Gang Luo, Keli Zheng, Lingyan Hu, Jing Wang, Kefeng Li

llms benchmarks

arXiv AI

Jul 7

CaresAI at SMM4H-HeaRD 2026: Predicting TNM Staging

arXiv:2607. 03466v1 Announce Type: cross Abstract: This study aims to predict Tumor, Node, and Metastasis (TNM) stage labels independently, with the Cancer Genome Atlas (TCGA) pathology report as the sixth shared task of SMM4H-HeaRD 2026.

By Joseph Itopa Abubakar, Jorge Jarme, Favour Igwezeke, Mary Adewunmi

rag

arXiv Machine Learning

5d ago

CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility

arXiv:2608. 12805v1 Announce Type: new Abstract: Access to clinical data is essential for developing reliable healthcare machine learning systems, but direct use of electronic health records is constrained by privacy regulation, institutional review, data-use agreements, and the risk of re-identification.

By Akanta Das, Al Amin Farhad, Mrinmoy Sarkar Anto, David Rehkopf, Ayin Vala, Tanmoy Sarkar Pias

benchmarks safety

arXiv Machine Learning

Jun 10

OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib

arXiv:2606. 11144v1 Announce Type: new Abstract: Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computational models on the corresponding longitudinal patient trajectories.

By Abhijoy Sarkar, Aarchi Singh Thakur

llms benchmarks

arXiv AI

Aug 3

ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics

arXiv:2603. 11872v3 Announce Type: replace-cross Abstract: Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI systems lack direct access to transcriptomic representations while expression foundation models remain opaque to natural language.

By Omar Coser

llms rag agents

arXiv Machine Learning

Jul 24

Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction

arXiv:2410. 00945v2 Announce Type: replace-cross Abstract: Gene-expression profiling is widely used in research and central to many areas of precision oncology, but remains costly and not universally accessible.

By Fredrik K. Gustafsson, Constance Boissin, Johan Vallon-Christersson, Mattias Rantalainen

arXiv AI

Jun 4

You Only Train Once: Differentiable Subset Selection for Omics Data

arXiv:2512. 17678v2 Announce Type: replace-cross Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling.

By Daphn\'e Chopard, Jorge da Silva Gon\c{c}alves, Irene Cannistraci, Thomas M. Sutter, Julia E. Vogt

efficiency benchmarks safety

arXiv Machine Learning

Jul 28

Metamorphic Testing for Clinical ML Models: A Framework Proposal and Pilot Study

arXiv:2607. 22984v1 Announce Type: cross Abstract: Machine learning models for clinical prediction tasks, such as in-hospital mortality and sepsis onset, routinely achieve high AUROC scores.

By Jie JW Wu, Feiyu E, Bo Chen

arXiv AI

Jul 2

Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies

arXiv:2607. 00472v1 Announce Type: cross Abstract: Cardiovascular disease is still one of the main causes of death around the world.

By Sagnik Ghosh

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