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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ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network
Abhijit Guha Roy,Sailesh Conjeti,Sri Phani Krishna Karri,Debdoot Sheet,Amin Katouzian,Christian Wachinger,Nassir Navab +6 more
TL;DR: In this article, a fully convolutional deep architecture, termed ReLayNet, is proposed for end-to-end segmentation of retinal layers and fluid masses in OCT scans.
Book ChapterDOI
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Abhijit Guha Roy,Abhijit Guha Roy,Sailesh Conjeti,Debdoot Sheet,Amin Katouzian,Nassir Navab,Nassir Navab,Christian Wachinger +7 more
TL;DR: SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that combines skip connections with the unpooling strategy for upsampling, is introduced and outperforms the latest state-of-the-art F- CNN models.
Proceedings ArticleDOI
Multiple instance learning of deep convolutional neural networks for breast histopathology whole slide classification
TL;DR: A new pooling layer is introduced that helps to aggregate most informative features from patches constituting a whole slide, without necessitating inter-patch overlap or global slide coverage, which helps the method to jointly learn to discover informative features locally as well as learn the classification margin globally.
Proceedings ArticleDOI
Simulating patho-realistic ultrasound images using deep generative networks with adversarial learning
Francis Tom,Debdoot Sheet +1 more
TL;DR: A generative adversarial network (GAN) inspired approach for fast simulation of patho-realistic ultrasound images with a visual Turing test indicating an equivocal confusion in discriminating simulated from real is proposed.
Proceedings ArticleDOI
Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography
TL;DR: This paper employs unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder and shows that ensemble training of auto-encoders fosters diversity in learning dictionary of visual kernels for vessel segmentation.