This paper introduces GLORIA, a novel framework for aligning and fusing histopathology, mRNA expression, and magnetic resonance imaging data for glioma grading and survival prediction.

The paper introduces a novel framework that aligns single white blood cell images with genetic data (karyotype and somatic mutations) to significantly improve the diagnosis of blood cancers, outperfor…

The paper introduces a simple, token-efficient vision-language model for generating comprehensive pathology synoptic reports from multiple whole-slide images (WSIs), achieving high performance while s…

Loom is a system for analyzing spatial transcriptomics data through detailed pseudo-temporal exploration, cross-sample comparisons, and investigation of spatiotemporal biological mechanisms.

The paper proposes GC-MoE, a novel framework that uses a Mixture-of-Experts approach guided by genomics to accurately predict cell-type-specific gene expression for individual cells from histopatholog…

CodeCytos is a novel coding-based reasoning agent framework that enables dynamic, programmable interaction with spatial molecular imaging data, significantly improving the automation and customization…

A fully automated, calibration-free pipeline is presented to align 2D hyperspectral information with the 3D shape of ex-vivo lumpectomy specimens using consumer-camera RGB images and a single top-down…

The paper proposes finetuning the Segment Anything Model (SAM) using large-scale synthetic fluorescence microscopy data to achieve robust and high-performing instance segmentation of mitochondria, add…

Agentic-J is a containerized, multi-agent AI assistant designed to enable biologists to perform complex, reproducible biological microscopy image analysis by specifying tasks in natural language.

The paper introduces JupOtter, a bug detection system for Jupyter Notebooks with three contributions: notebook-specific tokenization, cell-level bug prediction, and a labeled dataset called OtterDatas…

The paper presents a method for training deep learning classifiers on cancer registry data using Attention-Based Multiple Instance Learning (ABMIL), allowing the use of operationally-generated patient…

The paper introduces a histogram-regularized latent diffusion model to synthesize highly realistic and subtype-specific pulmonary nodules in 3D CT volumes, addressing the limitations of existing metho…

The paper introduces an integrated computational toolbox using topological and fractal analysis to quantitatively track microstructural changes during casein gelation, correlating these subtle changes…

The paper addresses 'Template Collapse' in 3D CT report generation—where models generate generic reports—by proposing CLarGen, a decoupled framework that significantly improves clinical accuracy and d…

The paper introduces retraining-free frameworks (Meow2X and TRNE) that mechanistically localize and suppress toxicity within language models by analyzing activation differences, achieving safety impro…