Anirban Mukhopadhyay
Cited by
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GANs for medical image analysis
S Kazeminia, C Baur, A Kuijper, B van Ginneken, N Navab, S Albarqouni, ...
Artificial Intelligence in Medicine 109, 101938, 2020
Knee menisci segmentation using convolutional neural networks: data from the osteoarthritis initiative
A Tack, A Mukhopadhyay, S Zachow
Osteoarthritis and cartilage 26 (5), 680-688, 2018
CATARACTS: Challenge on automatic tool annotation for cataRACT surgery
H Al Hajj, M Lamard, PH Conze, S Roychowdhury, X Hu, G Maršalkaitė, ...
Medical image analysis 52, 24-41, 2019
Statistical shape modeling of the left ventricle: myocardial infarct classification challenge
A Suinesiaputra, P Ablin, X Alba, M Alessandrini, J Allen, W Bai, S Cimen, ...
IEEE journal of biomedical and health informatics 22 (2), 503-515, 2017
Tool and phase recognition using contextual CNN features
M Sahu, A Mukhopadhyay, A Szengel, S Zachow
arXiv preprint arXiv:1610.08854, 2016
Addressing multi-label imbalance problem of surgical tool detection using CNN
M Sahu, A Mukhopadhyay, A Szengel, S Zachow
International journal of computer assisted radiology and surgery 12, 1013-1020, 2017
Toward an automatic preoperative pipeline for image-guided temporal bone surgery
J Fauser, I Stenin, M Bauer, WH Hsu, J Kristin, T Klenzner, J Schipper, ...
International journal of computer assisted radiology and surgery 14, 967-976, 2019
GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows
S Pati, SP Thakur, İE Hamamcı, U Baid, B Baheti, M Bhalerao, O Güley, ...
Communications Engineering 2 (1), 23, 2023
Endo-sim2real: Consistency learning-based domain adaptation for instrument segmentation
M Sahu, R Strömsdörfer, A Mukhopadhyay, S Zachow
International Conference on Medical Image Computing and Computer-Assisted …, 2020
An efficient Riemannian statistical shape model using differential coordinates: With application to the classification of data from the Osteoarthritis Initiative
C von Tycowicz, F Ambellan, A Mukhopadhyay, S Zachow
Medical image analysis 43, 1-9, 2018
Generative adversarial networks: A primer for radiologists
JM Wolterink, A Mukhopadhyay, T Leiner, TJ Vogl, AM Bucher, I Išgum
Radiographics 41 (3), 840-857, 2021
M3d-CAM: A PyTorch library to generate 3D attention maps for medical deep learning
K Gotkowski, C Gonzalez, A Bucher, A Mukhopadhyay
Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop on …, 2021
Detecting when pre-trained nnu-net models fail silently for covid-19 lung lesion segmentation
C Gonzalez, K Gotkowski, A Bucher, R Fischbach, I Kaltenborn, ...
Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021
Habitat-Net: Segmentation of habitat images using deep learning
JF Abrams, A Vashishtha, ST Wong, A Nguyen, A Mohamed, S Wieser, ...
Ecological informatics 51, 121-128, 2019
i3PosNet: instrument pose estimation from X-ray in temporal bone surgery
D Kügler, J Sehring, A Stefanov, I Stenin, J Kristin, T Klenzner, J Schipper, ...
International journal of computer assisted radiology and surgery 15, 1137-1145, 2020
Planning nonlinear access paths for temporal bone surgery
J Fauser, G Sakas, A Mukhopadhyay
International journal of computer assisted radiology and surgery 13, 637-646, 2018
A survey on shape-constraint deep learning for medical image segmentation
S Bohlender, I Oksuz, A Mukhopadhyay
IEEE Reviews in Biomedical Engineering, 2021
Unsupervised myocardial segmentation for cardiac BOLD
I Oksuz, A Mukhopadhyay, R Dharmakumar, SA Tsaftaris
IEEE transactions on medical imaging 36 (11), 2228-2238, 2017
Simulation-to-real domain adaptation with teacher–student learning for endoscopic instrument segmentation
M Sahu, A Mukhopadhyay, S Zachow
International journal of computer assisted radiology and surgery 16 (5), 849-859, 2021
Total variation random forest: Fully automatic mri segmentation in congenital heart diseases
A Mukhopadhyay
Reconstruction, Segmentation, and Analysis of Medical Images: First …, 2017
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