5 citations · 13 across the 8 of their papers we have counts for
6 papers · 1 filter
Bridging the gap between prostate radiology and pathology through machine learning
Indrani Bhattacharya, David S. Lim, Han Lin Aung +11
Prostate cancer is the second deadliest cancer for American men. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosi…
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Alessa Hering, Lasse Hansen, Tony C. W. Mok +50
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medi…
Weakly Supervised Registration of Prostate MRI and Histopathology Images
Wei Shao, Indrani Bhattacharya, Simon J. C. Soerensen +7
The interpretation of prostate MRI suffers from low agreement across radiologists due to the subtle differences between cancer and normal tissue. Image registration addresses this…
Geodesic Density Regression for Correcting 4DCT Pulmonary Respiratory Motion Artifacts
Wei Shao, Yue Pan, Oguz C. Durumeric +4
Pulmonary respiratory motion artifacts are common in four-dimensional computed tomography (4DCT) of lungs and are caused by missing, duplicated, and misaligned image data. This pap…
ProsRegNet: A Deep Learning Framework for Registration of MRI and Histopathology Images of the Prostate
Wei Shao, Linda Banh, Christian A. Kunder +10
Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observ…
CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis
Indrani Bhattacharya, Arun Seetharaman, Wei Shao +10
Magnetic Resonance Imaging (MRI) is widely used for screening and staging prostate cancer. However, many prostate cancers have subtle features which are not easily identifiable on…