activity
20212024
most citedhist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images

63 citations · 73 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CV20242 cited

Region Guided Attention Network for Retinal Vessel Segmentation

Syed Javed, Tariq M. Khan, Abdul Qayyum +2

Retinal imaging has emerged as a promising method of addressing this challenge, taking advantage of the unique structure of the retina. The retina is an embryonic extension of the…

cs.CV20241 cited

MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion

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

Survival risk stratification is an important step in clinical decision making for breast cancer management. We propose a novel deep learning approach for this purpose by integratin…

eess.IV20241 cited

Automatic 3D Multi-modal Ultrasound Segmentation of Human Placenta using Fusion Strategies and Deep Learning

Sonit Singh, Gordon Stevenson, Brendan Mein +2

Purpose: Ultrasound is the most commonly used medical imaging modality for diagnosis and screening in clinical practice. Due to its safety profile, noninvasive nature and portabili…

eess.IV2023

Assessing Encoder-Decoder Architectures for Robust Coronary Artery Segmentation

Shisheng Zhang, Ramtin Gharleghi, Sonit Singh +2

Coronary artery diseases are among the leading causes of mortality worldwide. Timely and accurate diagnosis, facilitated by precise coronary artery segmentation, is pivotal in chan…

eess.IV20231 cited

Attention and Pooling based Sigmoid Colon Segmentation in 3D CT images

Md Akizur Rahman, Sonit Singh, Kuruparan Shanmugalingam +4

Segmentation of the sigmoid colon is a crucial aspect of treating diverticulitis. It enables accurate identification and localisation of inflammation, which in turn helps healthcar…

cs.CV20233 cited

Visual Question Answering in the Medical Domain

Louisa Canepa, Sonit Singh, Arcot Sowmya

Medical visual question answering (Med-VQA) is a machine learning task that aims to create a system that can answer natural language questions based on given medical images. Althou…