Cardiovascular

MammoWise: Multi-Model Local RAG Pipeline for Mammography Report Generation

Screening mammography is high volume, time sensitive, and documentation heavy. Radiologists must translate subtle visual findings into consistent BI-RADS assessments, breast density categories, and structured narrative reports. While recent Vision Language Models (VLMs) enable image-to-text reporting, many rely on closed cloud systems or tightly coupled architectures that limit privacy, reproducib...

Momentum Memory for Knowledge Distillation in Computational Pathology

Multimodal learning that integrates genomics and histopathology has shown strong potential in cancer diagnosis, yet its clinical translation is hindered by the limited availability of paired histology-genomics data. Knowledge distillation (KD) offers a practical solution by transferring genomic supervision into histopathology models, enabling accurate inference using histology alone. However, exis...

Benchmarking Transfer Learning for Dense Breast Tissue Segmentation on Small Mammogram Datasets

Dense breast tissue diminishes the sensitivity of mammographic screening and is a key cancer risk factor, which motivates accurate segmentation under ...

Inference of cancer driver mutations from tumor microenvironmentcomposition: a pan-cancer study with cross-platform external validation

Cancer driver mutations shape the tumor microenvironment (TME), yet whether TME composition alone can predict genotype has not been systematically eva...

Survival risk heterogeneity among patients with NSCLC receiving nivolumab visualized by risk scores generated from deep learning method DeepSurv using tumor gene mutations

Immunotherapy with immune checkpoint inhibitors and immunotherapy combined with chemotherapy have represented promising treatments for NSCLC patients ...

LGD-Net: Latent-Guided Dual-Stream Network for HER2 Scoring with Task-Specific Domain Knowledge

It is a critical task to evalaute HER2 expression level accurately for breast cancer evaluation and targeted treatment therapy selection. However, the...

A NOVEL DEEP LEARNING MODEL, RDBCYCYLEGAN-CBAM FOR LOW-DOSE CT IMAGE DENOISING

Computed Tomography (CT) is one of the largest contributors to radiation exposure from medical imaging, which can induce DNA damage and increase cance...

Systematic Evaluation of Transfer Learning Strategies for Clinical Chemotherapy Response Prediction

Accurately predicting chemotherapy response remains a major challenge in precision oncology. Although machine-learning models based on tumour omics da...

A radiation-free screening system for adolescent idiopathic scoliosis using deep learning on 3D back surface point clouds

Widespread screening for Adolescent Idiopathic Scoliosis (AIS) is critical for timely intervention but is currently constrained by the radiation risks...

Deep Learning-Based Screening for POLE mutations on Histopathology Slides in Endometrial Cancer

POLE sequencing for somatic mutations (POLEmut) guides adjuvant therapy in endometrial cancer (EC), but cost and infrastructural considerations lead t...

ASPECT: Alternative Splicing Event Classification with Transformers

Motivation: Alternative splicing (AS) is a fundamental regulatory mechanism that expands transcriptomic and proteomic diversity by generating multiple...

Neural Networks as Entropic Systems: Applications in Digital Pathology

Deep learning systems in digital pathology are widely regarded as opaque, limiting clinical trust and interpretability. We present a framework for emp...

Nab-paclitaxel fused with the de novo designed receptor binder exhibits enhanced tumor targeting and therapeutic efficacy

Chemotherapy has been widely used in cancer treatment, but most of the chemotherapeutic drugs rely mainly on passive accumulation due to lack of targe...

A neural network model delivers a highly prognostic protein signature in cancer stem cells that identifies relapse in stage III colorectal cancer patients.

Background Stage III colorectal cancer poses a significant threat of metastasis development, as tumour resection and adjuvant chemotherapy do not guar...

Automated HER2 scoring with uncertainty quantification using lensfree holography and deep learning

Accurate assessment of human epidermal growth factor receptor 2 (HER2) expression is critical for breast cancer diagnosis, prognosis, and therapy sele...

Prognostic Risk Refinement using Artificial Intelligence in HR+/HER2- Early Breast Cancer: Implications for CDK4/6 Eligibility Criteria

Patient selection and enrolment into phase III randomized clinical trials (RCTs) of adjuvant cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitor therap...

Multimodal Fusion of Pathology Free-Text and Clinical Data Enhances Complication-Risk Discrimination After Implant-Based Breast Reconstruction

Implant-based breast reconstruction is the most common surgical option following mastectomy for breast cancer. Despite its prevalence, up to one-third...

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 a...

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...

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...