Deepak Anand

Deepak Anand

Data Science Manager, Nference (Mob: 8454912860)

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portfolio

publications

Classification of breast cancer histology using deep learning

Published in International Conference Image Analysis and Recognition, Springer, 2018

In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge by deep learning and provide an efficient patch-sampling strategy. Read more

Some new layer architectures for graph cnn

Published in arXiv preprint, 2018

The existing Graph CNN layers mostly neglect learning explicit operations for edge features while focusing on vertex features alone. We propose new formulations for convolutional, pooling, and fully connected layers for neural networks that make more comprehensive use of the information available in multi-dimensional graphs. Using these layers led to an improvement in classification accuracy over the state-of-the-art methods on benchmark graph datasets. Read more

Pixel-wise Segmentation of Right Ventricle of Heart

Published in IEEE TENCON, 2019

This paper proposes a deep learning based method for the accurate segmentation of right ventricle, which does not require post-processing and yet it achieves the state-of-the-art performance of 0.86 Dice coefficient and 6.73 mm Hausdorff distance on RVSC-MICCAI 2012 dataset. Read more

Histographs: Graphs in Histopathology

Published in SPIE Medical Imaging 2020, 2019

In this work, we propose to classify cancers using graph convolutional networks (GCNs) by modeling a tissue section as a multi-attributed spatial graph of its constituent cells. Read more

talks

Tutorial on Broad applications of Deep Learning in Electrical Engineering

Published:

More information here This is a joint talk by Prof. Amit Sethi and me. The agenda of the talk was to demonstrate some areas in the domain of electrical engineering where deep learning can be used as a computational tool. We presented some interesting use-cases like computation of fields in a complex environment, load prediction in grids, identification of modulations in channels, and some computer vision tasks. Read more

Conference proceedings talk on Fast GPU-Enabled Color Normalization for Digital Pathology

Published:

Normalizing unwanted color variations due to differences in staining processes and scanner responses has been shown to aid machine learning in computational pathology. Of the several popular techniques for color normalization, structure preserving color normalization (SPCN) is well-motivated, convincingly tested, and published with its code base. However, SPCN makes occasional errors in color basis estimation leading to artifacts such as swapping the color basis vectors between stains or giving a colored tinge to the background with no tissue. We made several algorithmic improvements to remove these artifacts. Additionally, the original SPCN code is not readily usable on gigapixel whole slide images (WSIs) due to long run times, use of proprietary software platform and libraries, and its inability to automatically handle WSIs. We completely rewrote the software such that it can automatically handle images of any size in popular WSI formats. Our software utilizes GPU-acceleration and open-source libraries that are becoming ubiquitous with the advent of deep learning. We also made several other small improvements and achieved a multifold overall speedup on gigapixel images, processing $10^9$ pixels in 3 minutes. Our algorithm and software is usable right out-of-the-box by the computational pathology community. Read more

Making Machines Learn

Published:

More information here This talk presents an overview of the domain of deep learning. It was aimed for first year graduate and undergraduate students of Electrical Engineering department of IIT Bombay. The resources for the talk are here. Read more

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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