Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Nikhil Cherian Kurian"'
Autor:
Abhijeet Patil, Harsh Diwakar, Jay Sawant, Nikhil Cherian Kurian, Subhash Yadav, Swapnil Rane, Tripti Bameta, Amit Sethi
Publikováno v:
Journal of Pathology Informatics, Vol 14, Iss , Pp 100306- (2023)
Histopathology whole slide images (WSIs) are being widely used to develop deep learning-based diagnostic solutions, especially for precision oncology. Most of these diagnostic softwares are vulnerable to biases and impurities in the training and test
Externí odkaz:
https://doaj.org/article/b97cf8ecd0304d6a8bb862ef89511703
Autor:
Deepak Anand, Nikhil Cherian Kurian, Shubham Dhage, Neeraj Kumar, Swapnil Rane, Peter H Gann, Amit Sethi
Publikováno v:
Journal of Pathology Informatics, Vol 11, Iss 1, Pp 19-19 (2020)
Context: Several therapeutically important mutations in cancers are economically detected using immunohistochemistry (IHC), which highlights the overexpression of specific antigens associated with the mutation. However, IHC panels can be imprecise an
Externí odkaz:
https://doaj.org/article/6cbaf26728b2403180378eda882c08c2
Autor:
Gregory Verghese, Mengyuan Li, Fangfang Liu, Amit Lohan, Nikhil Cherian Kurian, Swati Meena, Patrycja Gazinska, Aekta Shah, Aasiyah Oozeer, Terry Chan, Mark Opdam, Sabine Linn, Cheryl Gillett, Elena Alberts, Thomas Hardiman, Samantha Jones, Selvam Thavaraj, J Louise Jones, Roberto Salgado, Sarah E Pinder, Swapnil Rane, Amit Sethi, Anita Grigoriadis
Publikováno v:
The Journal of Pathology.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07fa2e1094fc334a625e95eafe0398e7
https://doi.org/10.1002/path.6088
https://doi.org/10.1002/path.6088
Autor:
Nikhil Cherian Kurian, Amit Lehan, Gregory Verghese, Nimish Dharamshi, Swati Meena, Mengyuan Li, Fangfang Liu, Cheryl Gillet, Swapnil Rane, Anita Grigoriadis, Amit Sethi
Although the U-Net architecture has been extensively used for segmentation of medical images, we address two of its shortcomings in this work. Firstly, the accuracy of vanilla U-Net degrades when the target regions for segmentation exhibit significan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aca9e3bbc1891d1f6197f0339e90449e
http://arxiv.org/abs/2205.01777
http://arxiv.org/abs/2205.01777
Publikováno v:
IEEE transactions on medical imaging. 41(4)
We had released MoNuSAC2020 as one of the largest publicly available, manually annotated, curated, multi-class, and multi-instance medical image segmentation datasets. Based on this dataset, we had organized a challenge at the International Symposium
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83a393bffa5b15ec08723462c3901f64
https://pubmed.ncbi.nlm.nih.gov/34086562
https://pubmed.ncbi.nlm.nih.gov/34086562
Publikováno v:
2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a0514501b4b41dfa57a1339b6c5496c
https://doi.org/10.1109/isbic56247.2022.9854644
https://doi.org/10.1109/isbic56247.2022.9854644
Autor:
Alexandr G. Rassadin, Ali Asghar Khani, Yinyin Yuan, Neeraj Kumar, Nikhil Cherian Kurian, Hasib Zunair, Hamid Behroozi, Priya Lakshmi Narayanan, Shuolin Liu, Romil Lodaya, Yanling Liu, Dwarikanath Mahapatra, Abhijeet Patil, Lata Kini, Pavel Semkin, Yuehan Yao, Hyun Jung, Seyed Alireza Fatemi Jahromi, Sanjay N. Talbar, Swapnil Rane, Ming Feng, Bhakti Baheti, G Thomas Brown, Vikas Ramachandra, Justin Law, Rupert Ecker, Xiyi Wu, Isabella Ellinger, Prasad Dutande, Tang-Kai Yin, Ehsan Montahaei, Shan E Ahmed Raza, Amirreza Mahbod, Aditya Mitkari, Abdessamad Ben Hamza, Hanyun Zhang, Zhengyu Xu, Kele Xu, Quoc Dang Vu, Yijie Huang, Bin Dong, Shikhar Srivastava, Lisheng Wang, Mahdieh Soleymani Baghshah, Nasir M. Rajpoot, Zhipeng Luo, Shuai Lv, Ujjwal Baid, Huai Chen, Ruchika Verma, Simon Graham, Lubomira Trnavska, Mieke Zwager, Steven Smiley, Amit Sethi, Abhiroop Tejomay, Dinesh Koka, Qi-Rui Fang
Publikováno v:
Ieee transactions on medical imaging, 40(12), 3413-3423. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d246f6bb68a09afa5970571e21e0d90a
https://research.rug.nl/en/publications/4639c9a9-8918-43fd-85fe-8ba1648b51e6
https://research.rug.nl/en/publications/4639c9a9-8918-43fd-85fe-8ba1648b51e6
Autor:
Nikhil Cherian, Kurian, Gurparkash, Singh, Poorvi, Hebbar, Shreekanya, Kodate, Swapnil, Rane, Amit, Sethi
Publikováno v:
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
Deep learning (DL) thrives on the availability of a large number of high quality images with reliable labels. Due to the large size of whole slide images in digital pathology, patches of manageable size are often mined for use in DL models. These pat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21391bca463133f1471fe6a4a0c490a8
https://doi.org/10.1109/embc46164.2021.9630477
https://doi.org/10.1109/embc46164.2021.9630477
Publikováno v:
ISBI
The accuracy of deep learning classifiers trained using the cross entropy loss function suffers even when a fraction of training labels are wrong or input images are uninformative. Training images and labels for computational pathology are often nois
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::567179020d100916986a90a695feb217
https://doi.org/10.1109/isbi48211.2021.9434169
https://doi.org/10.1109/isbi48211.2021.9434169
Autor:
Amit Sethi, Sunil Patel, Nikhil Cherian Kurian, Mohd. Talha, Abhijeet Patil, Sammed Mangale, Aniket Bhatia
Publikováno v:
ISBI
Performance of deep learning algorithms decreases drastically if the data distributions of the training and testing sets are different. Due to variations in staining protocols, reagent brands, and habits of technicians, color variation in digital his
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2273ffa860286b6506aecb0952b26bd6
https://doi.org/10.1109/isbi48211.2021.9434121
https://doi.org/10.1109/isbi48211.2021.9434121