AI Medical Compendium

New causal discovery algorithm over censored variables identifies subtype-specific drivers of breast cancer progression.

GigaScience May 22, 2026

Many research domains are producing large, multi-scale, multi-modal datasets at growing rates with mixed variable types (continuous, discrete, censored). Identifying possible cause-effect associations in such datasets is essential for predicting outc... read more

PathoFact 2.0: An Integrative Pipeline for the Prediction of Antimicrobial Resistance Genes, Virulence Factors, Toxins and Toxin-associated Proteins, and Biosynthetic Gene Clusters in Metagenomes.

GigaScience May 22, 2026

BACKGROUND: Antimicrobial resistance genes (ARG) and virulence factors (VFs) are central contributors to the global health crisis surrounding drug-resistant infections. FINDINGS: We introduce PathoFact 2.0, an enhanced pipeline for improved ARG, VF, ... read more

Tackling complexity in crystal structure determination of metal organic compounds using machine learning interatomic potentials.

Chemical communications (Cambridge, England) May 22, 2026

We integrate ab initio crystal structure prediction with universal machine learning interatomic potentials (UMA and Orb-v3) to determine challenging metal organic compound structures. Our framework successfully resolves the previously unknown, techno... read more

ISTASTrack: Bridging ANN and SNN via ISTA Adapter for RGB-Event Tracking.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society May 22, 2026

RGB-Event tracking has become a promising trend in visual object tracking to leverage the complementary strengths of both RGB images and dynamic spike events for improved performance. However, existing artificial neural networks (ANNs) struggle to fu... read more

A Digital Twin-Inspired Closed-Loop Latent Simulation Framework for Cross-Cohort Breast Cancer Subtype Classification under Modality-Disjoint Learning.

IEEE journal of biomedical and health informatics May 22, 2026

Breast cancer PAM50 subtype classification is hindered by the single-pass prediction paradigm of existing deep learning systems, which provide no mechanism for iterative representation refinement or uncertainty trajectory analysis. We present the Cro... read more

A Variational Mean-Field Control Framework for Graph Representation Learning.

IEEE transactions on pattern analysis and machine intelligence May 22, 2026

Feature representation learning in graph neural networks (GNNs) is a dynamic process driven by progressive information exchange throughout the graph. Current GNNs typically apply pre-defined message-passing heuristics uniformly across all graph data,... read more

Convolutional Neural Networks in Radiology: Principles, Clinical Applications, and a Practical Framework for Radiologists.

Medical principles and practice : international journal of the Kuwait University, Health Science Centre May 22, 2026

Artificial intelligence is increasingly embedded within radiology workflows. In radiology, large language models may support reporting and communication tasks, while machine learning and deep learning models are increasingly used for image classifica... read more

An automatic detection model for spread through air spaces in postoperative pathological sections based on deep learning in NSCLC.

Computer assisted surgery (Abingdon, England) May 22, 2026

Spread through air spaces (STAS) is recognized as an aggressive pattern of invasion in lung cancer and has been associated with poorer survival outcomes. However, STAS is frequently overlooked or misdiagnosed during routine pathological diagnosis. We... read more

TransitNet: A lightweight semantic segmentation network for urban traffic scene understanding.

PloS one May 22, 2026

Existing semantic segmentation networks often suffer from large parameter sizes and high computational complexity, making it difficult to deploy them on resource-constrained in-vehicle systems or edge devices. Additionally, these networks lack suffic... read more

LeafDet: A lightweight and interpretable deep learning framework for tomato leaf disease detection.