S
Suthirth Vaidya
Researcher at Indian Institutes of Technology
Publications - 5
Citations - 304
Suthirth Vaidya is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Nodule (medicine) & Cancer. The author has an hindex of 3, co-authored 5 publications receiving 200 citations.
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Journal ArticleDOI
Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.
Aaron Carass,Snehashis Roy,Amod Jog,Jennifer L. Cuzzocreo,Elizabeth Magrath,Adrian Gherman,Julia Button,James Nguyen,Ferran Prados,Carole H. Sudre,Manuel Jorge Cardoso,Niamh Cawley,Olga Ciccarelli,Claudia A. M. Wheeler-Kingshott,Sebastien Ourselin,Laurence Catanese,Hrishikesh Deshpande,Pierre Maurel,Olivier Commowick,Christian Barillot,Xavier Tomas-Fernandez,Xavier Tomas-Fernandez,Simon K. Warfield,Simon K. Warfield,Suthirth Vaidya,Abhijith Chunduru,Ramanathan Muthuganapathy,Ganapathy Krishnamurthi,Andrew Jesson,Tal Arbel,Oskar Maier,Heinz Handels,Leonardo O. Iheme,Devrim Unay,Saurabh Jain,Diana M. Sima,Dirk Smeets,Mohsen Ghafoorian,Bram Platel,Ariel Birenbaum,Hayit Greenspan,Pierre-Louis Bazin,Peter A. Calabresi,Ciprian M. Crainiceanu,Lotta Maria Ellingsen,Lotta Maria Ellingsen,Daniel S. Reich,Jerry L. Prince,Dzung L. Pham +48 more
TL;DR: A quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms are presented.
Journal ArticleDOI
Unboxing AI - Radiological Insights Into a Deep Neural Network for Lung Nodule Characterization.
Vasantha Kumar Venugopal,Kiran Vaidhya,Murali Murugavel,Abhijith Chunduru,Vidur Mahajan,Suthirth Vaidya,Digvijay Mahra,Akshay Rangasai,Harsh Mahajan +8 more
TL;DR: In this article, a 20-layer deep residual CNN was trained on 1245 Chest CTs from National Lung Screening Trial (NLST) trial to predict the malignancy risk of a nodule.
Posted Content
Unboxing AI - Radiological Insights Into a Deep Neural Network for Lung Nodule Characterization
Vasantha Kumar Venugopal,Kiran Vaidhya,Abhijith Chundur,Vidur Mahajan,Murali Murugavel,Suthirth Vaidya,Digvijay Mahra,Akshay Rangasai,Harsh Mahajan +8 more
TL;DR: The potential ability of a radiologist to visually parse the deep learning algorithm generated "heat map" to identify features aiding classification is discussed, to explain predictions of a deep residual convolutional network for characterization of lung nodule by analyzing heat maps.
Book ChapterDOI
An Automated Workflow for Lung Nodule Follow-Up Recommendation Using Deep Learning
Krishna Chaitanya Kaluva,Kiran Vaidhya,Abhijith Chunduru,Sambit Tarai,Sai Prasad Pranav Nadimpalli,Suthirth Vaidya +5 more
TL;DR: An automated workflow for follow-up recommendation based on low-dose computed tomography (LDCT) images using deep learning, as per 2017 Fleischner Society guidelines is proposed, finding that rule-based approach for following-up alongside deep learning models is the best approach in achieving best results.
Journal ArticleDOI
LNDb challenge on automatic lung cancer patient management.
João Pedrosa,Guilherme Aresta,Carlos Abreu Ferreira,Gurraj Atwal,Hady Ahmady Phoulady,Xiaoyu Chen,Rongzhen Chen,Jiaoliang Li,Liansheng Wang,Adrian Galdran,Hamid Bouchachia,Krishna Chaitanya Kaluva,Kiran Vaidhya,Abhijith Chunduru,Sambit Tarai,Sai Prasad Pranav Nadimpalli,Suthirth Vaidya,Ildoo Kim,Alexandr G. Rassadin,Zhenhuan Tian,Zhongwei Sun,Yizhuan Jia,Xuejun Men,Isabel Ramos,Antonio José Ledo Alves da Cunha,Aurélio Campilho +25 more
TL;DR: The Lung Nodule Database (LNDb) Challenge as mentioned in this paper addressed lung nodule detection, segmentation and characterization as well as prediction of patient follow-up according to the 2017 Fleischner society pulmonary nodule guidelines.