J
Jesper Molin
Researcher at Chalmers University of Technology
Publications - 25
Citations - 610
Jesper Molin is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Digital pathology & Deep learning. The author has an hindex of 9, co-authored 23 publications receiving 446 citations. Previous affiliations of Jesper Molin include Linköping University.
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Journal ArticleDOI
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.
Mitko Veta,Yujing J. Heng,Nikolas Stathonikos,Babak Ehteshami Bejnordi,Francisco Beca,Thomas Wollmann,Karl Rohr,Manan Shah,Dayong Wang,Mikael Rousson,Martin Hedlund,David Tellez,Francesco Ciompi,Erwan Zerhouni,David Lanyi,Matheus P. Viana,Vassili Kovalev,Vitali Liauchuk,Hady Ahmady Phoulady,Talha Qaiser,Simon Graham,Nasir M. Rajpoot,Erik Sjöblom,Jesper Molin,Kyunghyun Paeng,Sangheum Hwang,Sunggyun Park,Zhipeng Jia,Eric Chang,Yan Xu,Andrew H. Beck,Paul J. van Diest,Josien P. W. Pluim +32 more
TL;DR: The achieved results are promising given the difficulty of the tasks and weakly‐labeled nature of the ground truth, however, further research is needed to improve the practical utility of image analysis methods for this task.
Journal ArticleDOI
Implementation of large-scale routine diagnostics using whole slide imaging in Sweden: Digital pathology experiences 2006-2013
TL;DR: The fact that two full-scale digital systems have been implemented and that a large portion of the primary reporting is voluntarily performed digitally shows that large-scale digitization is possible today.
Journal ArticleDOI
Deep learning nuclei detection: A simple approach can deliver state-of-the-art results.
TL;DR: The basic PMap approach is greatly affected by certain parameters and guidance is provided on their impact and best settings, which can yield equal detection quality as more complex and time-consuming state-of-the-art approaches.
Proceedings ArticleDOI
Towards grading gleason score using generically trained deep convolutional neural networks
TL;DR: An automatic algorithm is developed to assist pathologists to report Gleason score on malignant prostatic adenocarcinoma specimen and makes a spatial classification of the whole slide into the same growth patterns as pathologists do.