85 citations · 223 across the 29 of their papers we have counts for
11 papers · 1 filter
ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation
Mingxuan Gu, Sulaiman Vesal, Mareike Thies +5
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain. Contrastive learning (CL) in the context of UDA…
Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation
Sulaiman Vesal, Mingxuan Gu, Ronak Kosti +2
Deep learning models are sensitive to domain shift phenomena. A model trained on images from one domain cannot generalise well when tested on images from a different domain, despit…
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Zhaohan Xiong, Qing Xia, Zhiqiang Hu +41
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial fir…
A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT
Sulaiman Vesal, Nishant Ravikumar, Andreas Maier
Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer. Acc…
A Multi-task Framework for Skin Lesion Detection and Segmentation
Sulaiman Vesal, Shreyas Malakarjun Patil, Nishant Ravikumar +1
Early detection and segmentation of skin lesions is crucial for timely diagnosis and treatment, necessary to improve the survival rate of patients. However, manual delineation is t…
Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI
Sulaiman Vesal, Nishant Ravikumar, Andreas Maier
Segmentation of the left atrial chamber and assessing its morphology, are essential for improving our understanding of atrial fibrillation, the most common type of cardiac arrhythm…
SkinNet: A Deep Learning Framework for Skin Lesion Segmentation
Sulaiman Vesal, Nishant Ravikumar, Andreas Maier
There has been a steady increase in the incidence of skin cancer worldwide, with a high rate of mortality. Early detection and segmentation of skin lesions are crucial for timely d…
Classification of breast cancer histology images using transfer learning
Sulaiman Vesal, Nishant Ravikumar, AmirAbbas Davari +2
Breast cancer is one of the leading causes of mortality in women. Early detection and treatment are imperative for improving survival rates, which have steadily increased in recent…
Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation
Sulaiman Vesal, Nishant Ravikumar, Stephan Ellman +1
Accurate segmentation of breast lesions is a crucial step in evaluating the characteristics of tumors. However, this is a challenging task, since breast lesions have sophisticated…
Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings
AmirAbbas Davari, Nikolaos Sakaltras, Armin Haeberle +4
Old master drawings were mostly created step by step in several layers using different materials. To art historians and restorers, examination of these layers brings various insigh…
Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation
Sulaiman Vesal, Andres Diaz-Pinto, Nishant Ravikumar +3
Magnetic resonance imaging (MRI) is an effective imaging modality for identifying and localizing breast lesions in women. Accurate and precise lesion segmentation using a computer-…