Published in IEEE WIECON 2019, 2019
This paper presents a multiple-instance learning based method for classifcation and localization of breast cancer in histopathology images. Read more
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
Published in Transaction of Medical Imaging, IEEE, 2019
In this paper, we present the key findings of MoNuSeg challenge at MICCAI 2018 Read more
Published in ESMO MAP, 2019
This paper provides a Bayesian framework for quantifying survival uncertainity using deep esembles. The input to network is PAM50 gene set and clinical variables. Read more
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
Published in (Accepted) 26th International Conference on Systems, Signals and Image Processing, IWSSIP 2019, 2019
This paper presents a GPU-based color-normalization method for whole-slide gigapixel images in digital pathology. Read more
Published in arXiv preprint, 2019
This paper presents a low latency, robust and scalable neural net based decoder for convolutional and low-density parity-check (LPDC) coding schemes. Read more
Published in Proceedings of the San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX., 2018
This paper provides a proof-of-concept for detection of genetic mutation in breast cancer patients from H&E images instead of using costly IHC images. Read more
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
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
Published in IEEE 56th Annual Conference on Decision and Control (CDC), 2017
This paper deals with leader selection in multi-agent networks for finite-time concensus. Read more