Manit Zaveri
University of Waterloo
7 Papers
2 Citations
Manit Zaveri is an academic researcher from University of Waterloo. The author has contributed to research in topics: Computer science & Magnification. The author has an hindex of 1, co-authored 6 publications.
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Papers
Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides.
Abtin Riasatian,Morteza Babaie,Danial Maleki,Shivam Kalra,Mojtaba Valipour,Sobhan Hemati,Manit Zaveri,Amir Safarpoor,Sobhan Shafiei,Mehdi Afshari,Maral Rasoolijaberi,Milad Sikaroudi,Mohd Adnan,Sultaan Shah,Charles Choi,Savvas Damaskinos,Clinton J. V. Campbell,Phedias Diamandis,Liron Pantanowitz,Hany Kashani,Ali Ghodsi,Hamid R. Tizhoosh +21 more
TL;DR: KimiaNet as discussed by the authors employs the topology of the DenseNet with four dense blocks, fine-tuned and trained with histopathology images in different configurations, using high-cellularity mosaic approach to enable the usage of weak labels of 7126 whole slide images of formalin-fixed paraffin-embedded human pathology samples publicly available through The Cancer Genome Atlas (TCGA) repository.
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•Posted Content
Recognizing Magnification Levels in Microscopic Snapshots
Manit Zaveri,Shivam Kalra,Morteza Babaie,Sultaan Shah,Savvas Damskinos,Hany Kashani,Hamid R. Tizhoosh +6 more
TL;DR: This paper extracts deep features of the images available on TCGA dataset with known magnification to train a classifier for magnification recognition, and compared the results with LBP, a well-known handcrafted feature extraction method.
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Recognizing Magnification Levels in Microscopic Snapshots
Manit Zaveri,Shivam Kalra,Morteza Babaie,Sultaan Shah,Savvas Damskinos,Hany Kashani,Hamid R. Tizhoosh +6 more
- 01 Jul 2020
TL;DR: In this article, a multi-layer perceptron was trained as a classifier for magnification recognition in the TCGA dataset, which achieved a mean accuracy of 96% with LBP.
Kimia-5MAG – A Dataset for Learning the Magnification in Histopathology Images
Manit Zaveri,Sobhan Hemati,Sultaan Shah,Savvas Damskinos,Hamid R. Tizhoosh +4 more
- 01 Nov 2020
TL;DR: Kimia-5MAG as discussed by the authors is a dataset consisting of 33,345 patches at 5 different magnification classes created from WSIs made publicly available by The Cancer Genome Atlas (TCGA).
3
•Posted Content
Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slides
Abtin Riasatian,Morteza Babaie,Danial Maleki,Shivam Kalra,Mojtaba Valipour,Sobhan Hemati,Manit Zaveri,Amir Safarpoor,Sobhan Shafiei,Mehdi Afshari,Maral Rasoolijaberi,Milad Sikaroudi,Mohd Adnan,Sultaan Shah,Charles Choi,Savvas Damaskinos,Clinton J. V. Campbell,Phedias Diamandis,Liron Pantanowitz,Hany Kashani,Ali Ghodsi,Hamid R. Tizhoosh +21 more
TL;DR: KimiaNet as discussed by the authors employs the topology of the DenseNet with four dense blocks, fine-tuned and trained with histopathology images in different configurations, and uses high-cellularity mosaic approach to enable the usage of weak labels of 7,126 whole slide images of formalin-fixed paraffin-embedded human pathology samples publicly available through the Cancer Genome Atlas (TCGA) repository.