pathology

GATE-ST proposes a gene-aware text-image encoder that integrates text-encoded gene summaries with histology image embeddings via cross-attention to improve spatial gene expression prediction, outperforming image-only and random-embedding baselines in pathology imaging benchmarks.

SlideBank is a training-free framework for whole-slide image reasoning in pathology, using a persistent hierarchical evidence bank to improve consistency and reduce inference costs.

GigaPath-Flash and GigaTIME-Flash are efficient pathology foundation models that reduce computational demands while maintaining performance, enabling larger-scale studies in computational pathology.

The article examines how genetic mutations associated with autism contribute to the development of neurodevelopmental disorders.

The article discusses the risk of transmitting amyloid β pathology, associated with Alzheimer's disease, through transfused blood products.

Microsoft Research and Paige introduce PRISM2, a multimodal foundation model trained on pathology images and language, which matches specialized cancer-detection systems across benchmarks without task-specific models. The model weights are publicly available on Hugging Face for research.

An autopsy study found replicating SARS-CoV-2 in the hearts of Long COVID patients, associated with cardiac symptoms and gene expression changes, suggesting viral persistence drives Long COVID cardiac pathology.

This paper introduces the Nimblemind Multi-Agent System (nMAS) for extracting evidence of H. pylori infection from gastric biopsy reports, achieving 98.61% accuracy across 216 feature-case decisions and demonstrating substantial time savings over manual review.

Introduces a distribution-based multiple instance learning framework using zero-inflated beta modeling to improve tumor proportion score prediction in non-small cell lung cancer from histopathology slides.

PathPocket is a multimodal AI agentic co-pilot for evidence-grounded pathology, utilizing a comprehensive evidence corpus and hypergraph to outperform existing state-of-the-art methods on over 200,000 real-world cases.

PathoSage introduces a three-stage framework for pathology multimodal reasoning that separates knowledge retrieval, evidence collection, and evidence adjudication to reduce hallucinations and handle conflicting evidence, featuring a training-free Beta-Bernoulli experience system for modeling tool reliability.

A medical professional shares their positive experience using ChatGPT to assist in diagnostic pathology, demonstrating the AI's ability to provide accurate and detailed analysis comparable to a dermatopathologist.