Oncology/Hematology

Predicting Protein Cascade Expression from H&E Images

Protein expression within oncogenic or suppressive pathways is a hallmark indicator of oncogenesis. While traditional AI models in digital pathology attempt to predict singular proteins, there is a need to predict the downstream expression of proteins to indicate the propagation of signals. RNA expression provides novel information, but does not provide information about the downstream propagation...

Generative modeling reveals the connection between cellular morphology and gene expression

The understanding of how transcriptional programs give rise to cellular morphology, and how morphological features reflect and influence cell identity and function remains limited. This is due in part to the lack of large-scale datasets pairing the two modalities as well as the absence of computational frameworks capable of modeling their cross-modal structure. Here, we introduce COSMIC, a bidirec...

A Mobile AI-enhanced Platform for Standardized Wound Assessment and Clinical Decision Support

Chronic wounds affect over 1.2 million Canadians and incur healthcare costs exceeding $13 billion annually, with global expenditures approaching $149 ...

Ensemble Machine Learning Approaches Predict Survival in Lower-Grade Glioma Based on Glycosphingolipid Gene Expression and Metabolic Modelling

Glycosphingolipids (GSLs) are essential components of biological membranes with important roles in cell signalling. Disrupted GSL metabolism is associ...

Predicting Gene Mutations in Colon Cancer Using Long-Term Temporal Dependency Learning on a Directed Co-Occurrence Asymmetry Graph

Accurate prediction of mutational dependencies to model tumor evolution can improve our understanding of cancer progression and is crucial for early d...

TCRAD: An End-to-End Framework for Antigen-Targeted T Cell Receptor Design

Understanding and engineering T-cell receptor (TCR) specificity is central to personalized immunotherapy and antigen discovery. However, while antigen...

Reliable Brain Tumor Segmentation Based on Spiking Neural Networks with Efficient Training

We propose a reliable and energy-efficient framework for 3D brain tumor segmentation using spiking neural networks (SNNs). A multi-view ensemble of sa...

Semi-Supervised Domain Adaptation with Latent Diffusion for Pathology Image Classification

Deep learning models in computational pathology often fail to generalize across cohorts and institutions due to domain shift. Existing approaches eith...

Reprogramming BCMA-Targeted CAR-T Cells through γ-Secretase Modulation Blocks Antigen Shedding and Extends CAR-T Longevity

B-cell maturation antigen (BCMA) shedding by {gamma}-secretase generates soluble BCMA (sBCMA) , which diminishes membrane antigen density, and limits ...

Retrospective multi-cohort validation of a real-world transcriptomics-guided machine learning model for treatment response prediction in breast cancer

Selection of systemic therapy for breast cancer remains largely empirical, particularly for chemotherapy, due to the lack of robust biomarkers that pr...

I3LUNG: Clinical Validation of a Multimodal AI Tool to Support Immunotherapy Decisions in NSCLC

Despite a decade of immunotherapy, treatment selection in non-small cell lung cancer (NSCLC) still relies on subgroup analyses and clinical scores. I3...

Integrating Quantitative Histology with Clinical Data Improves Prediction of Cervical Intraepithelial Neoplasia Regression

Cervical intraepithelial neoplasia grade 2 (CIN2) lesions show variable outcomes, and accurate prediction of regression remains a major clinical chall...

Sub-Region-Aware Modality Fusion and Adaptive Prompting for Multi-Modal Brain Tumor Segmentation

The successful adaptation of foundation models to multi-modal medical imaging is a critical yet unresolved challenge. Existing models often struggle t...

PMPBench: A Paired Multi-Modal Pan-Cancer Benchmark for Medical Image Synthesis

Contrast medium plays a pivotal role in radiological imaging, as it amplifies lesion conspicuity and improves detection for the diagnosis of tumor-rel...

Peripheral blood profiles reflecting progenitor lineage balance predict treatment response in chronic myeloid leukemia

Early achievement of deep remission improves patients' outcome in chronic myeloid leukemia (CML) treatment, highlighting the need for predictive indic...

VAETracer: Mutation-Guided Lineage Reconstruction and Generational State Inference from scRNA-seq

Somatic mutations accumulate with cell division and are key to understanding tumor evolution. While single-cell RNA sequencing (scRNA-seq) can effecti...

Transfer Learning from One Cancer to Another via Deep Learning Domain Adaptation

Supervised deep learning models often achieve excellent performance within their training distribution but struggle to generalize beyond it. In cancer...

Federated Transformer-GNN for Privacy-Preserving Brain Tumor Localization with Modality-Level Explainability

Deep learning models for brain tumor analysis require large and diverse datasets that are often siloed across healthcare institutions due to privacy r...

Spatial Decoding of Tertiary Lymphoid Structure Maturation in Non-Small Cell Lung Cancer Using Deep Neural Networks

Understanding the role of tertiary lymphoid structures (TLS) is crucial in non-small cell lung cancer (NSCLC), as they are associated with patient pro...

Medea: An omics AI agent for therapeutic discovery

AI agents promise to empower biomedical discovery, but realizing this promise requires the ability to complete transparent, long-horizon analyses usin...