output
20232026
most citedhist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images

63 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

5 papers

cs.CL2026

CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications

Victoria Blake, Jamie Novak, Mathew Miller +2

Background: Clinical named entity recognition tools commonly map free text to Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). For many downstream tasks, h…

cs.LG2024★ 25 cited

Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learning

Annette Spooner, Mohammad Karimi Moridani, Azadeh Safarchi +4

The complementary information found in different modalities of patient data can aid in more accurate modelling of a patient's disease state and a better understanding of the underl…

cs.CV2024★ 27 cited

BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion

Raktim Kumar Mondol, Ewan K. A. Millar, Arcot Sowmya +1

Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment de…

cs.CV2023★ 63 cited

hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images

Raktim Kumar Mondol, Ewan K. A. Millar, Peter H Graham +3

Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemist…

eess.IV2023★ 57 cited

Breast Cancer Histopathology Image based Gene Expression Prediction using Spatial Transcriptomics data and Deep Learning

Md Mamunur Rahaman, Ewan K. A. Millar, Erik Meijering

Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they pro…