D
Debdoot Sheet
Researcher at Indian Institute of Technology Kharagpur
Publications - 129
Citations - 2813
Debdoot Sheet is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Convolutional neural network & Segmentation. The author has an hindex of 19, co-authored 121 publications receiving 1824 citations. Previous affiliations of Debdoot Sheet include Jadavpur University & Ludwig Maximilian University of Munich.
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Proceedings ArticleDOI
Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images
TL;DR: This work presents a computational imaging framework using deep and ensemble learning based hybrid architecture for reliable detection of blood vessels in fundus color images and achieves the objective of vessel detection with max.
Proceedings ArticleDOI
Classifying histopathology whole-slides using fusion of decisions from deep convolutional network on a collection of random multi-views at multi-magnification
TL;DR: A deep convolutional neural network (CNN) based solution is proposed, where images from random number of regions of the tissue section at multiple magnifications are analysed without any necessity of view correspondence across magnifications.
Journal ArticleDOI
Evaluation of Commonly Used Algorithms for Thyroid Ultrasound Images Segmentation and Improvement Using Machine Learning Approaches
TL;DR: This work compares three different nonautomatic segmentation algorithms in freehand three-dimensional ultrasound imaging in terms of accuracy, robustness, ease of use, level of human interaction required, and computation time.
Proceedings ArticleDOI
Learning Latent Temporal Connectionism of Deep Residual Visual Abstractions for Identifying Surgical Tools in Laparoscopy Procedures
TL;DR: A method which learns to detect the tool presence in laparoscopy videos by leveraging the temporal connectionist information in a systematically executed surgical procedures by learning the long and short order relationships between higher abstractions of the spatial visual features extracted from the surgical video is proposed.
Journal ArticleDOI
Lumen Segmentation in Intravascular Optical Coherence Tomography Using Backscattering Tracked and Initialized Random Walks
Abhijit Guha Roy,Sailesh Conjeti,Stephane Carlier,Pranab K. Dutta,Adnan Kastrati,Andrew F. Laine,Nassir Navab,Amin Katouzian,Debdoot Sheet +8 more
TL;DR: High segmentation accuracy and consistency substantiates the characteristics of this method to reliably segment lumen across pullbacks in the presence of vulnerability cues and necrotic pool and has a deterministic finite time-complexity.