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Chetan L. Srinidhi
Researcher at University of Toronto
Publications - 9
Citations - 491
Chetan L. Srinidhi is an academic researcher from University of Toronto. The author has contributed to research in topics: Feature learning & Feature (machine learning). The author has an hindex of 5, co-authored 8 publications receiving 231 citations. Previous affiliations of Chetan L. Srinidhi include National Institute of Technology, Karnataka & Sunnybrook Research Institute.
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Deep neural network models for computational histopathology: A survey
TL;DR: A comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis can be found in this paper, where a survey of over 130 papers is presented.
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Recent Advancements in Retinal Vessel Segmentation
TL;DR: A systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years is carried out and provides an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.
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Automated Method for Retinal Artery/Vein Separation via Graph Search Metaheuristic Approach
TL;DR: A novel graph search metaheuristic approach for automatic separation of arteries/veins (A/V) from color fundus images that outperforms the state-of-the-art methods for A/V separation.
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Self-supervised driven consistency training for annotation efficient histopathology image analysis.
Chetan L. Srinidhi,Chetan L. Srinidhi,Seung Wook Kim,Fu-Der Chen,Anne L. Martel,Anne L. Martel +5 more
TL;DR: In this paper, a self-supervised pretext task was proposed to learn a powerful supervisory signal for unsupervised representation learning, and a new teacher-student semisupervised consistency paradigm was introduced to learn to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific unlabeled data.
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A visual attention guided unsupervised feature learning for robust vessel delineation in retinal images
TL;DR: The VA-UFL approach is shown to be robust to segmentation of thin vessels, strong central vessel reflex, complex crossover structures and fares well on abnormal cases, and can be easily integrated into large-scale retinal screening programs where the expensive labelled annotation is often unavailable.