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Ganapathy Krishnamurthi

Researcher at Indian Institute of Technology Madras

Publications -  66
Citations -  4174

Ganapathy Krishnamurthi is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Convolutional neural network & Segmentation. The author has an hindex of 20, co-authored 62 publications receiving 2465 citations. Previous affiliations of Ganapathy Krishnamurthi include Indian Institutes of Technology & Case Western Reserve University.

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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
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Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard, +36 more
- 17 May 2018 - 
TL;DR: How far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies is measured, to open the door to highly accurate and fully automatic analysis of cardiac CMRI.
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Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.

Aaron Carass, +48 more
- 01 Mar 2017 - 
TL;DR: A quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms are presented.
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Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers

Mahendra Khened, +2 more
- 01 Jan 2019 - 
TL;DR: In this paper, the authors proposed a novel up-sampling path which incorporates long skip and short-cut connections to overcome the feature map explosion in conventional FCN based architectures.
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Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm.

U. Rajendra Acharya, +6 more
- 01 Jul 2012 - 
TL;DR: A novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers are used to determine parameters that classify normal and FLD-affected abnormal livers.