| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1010 | 2018 |
| Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? O Bernard, A Lalande, C Zotti, F Cervenansky, X Yang, PA Heng, I Cetin, ... IEEE transactions on medical imaging 37 (11), 2514-2525, 2018 | 703 | 2018 |
| Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers M Khened, VA Kollerathu, G Krishnamurthi Medical image analysis 51, 21-45, 2019 | 232 | 2019 |
| Longitudinal multiple sclerosis lesion segmentation: resource and challenge A Carass, S Roy, A Jog, JL Cuzzocreo, E Magrath, A Gherman, J Button, ... NeuroImage 148, 77-102, 2017 | 220 | 2017 |
| Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm UR Acharya, SV Sree, R Ribeiro, G Krishnamurthi, RT Marinho, ... Medical physics 39 (7Part1), 4255-4264, 2012 | 104 | 2012 |
| Densely connected fully convolutional network for short-axis cardiac cine MR image segmentation and heart diagnosis using random forest M Khened, V Alex, G Krishnamurthi International Workshop on Statistical Atlases and Computational Models of …, 2017 | 78 | 2017 |
| Generative adversarial networks for brain lesion detection V Alex, MS KP, SS Chennamsetty, G Krishnamurthi Medical Imaging 2017: Image Processing 10133, 113-121, 2017 | 74 | 2017 |
| Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation V Alex, K Vaidhya, S Thirunavukkarasu, C Kesavadas, G Krishnamurthi Journal of Medical Imaging 4 (4), 041311, 2017 | 64 | 2017 |
| Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization UR Acharya, O Faust, APC Alvin, G Krishnamurthi, JCR Seabra, ... Computer methods and programs in biomedicine 110 (1), 66-75, 2013 | 61 | 2013 |
| 2D-densely connected convolution neural networks for automatic liver and tumor segmentation KC Kaluva, M Khened, A Kori, G Krishnamurthi arXiv preprint arXiv:1802.02182, 2018 | 55 | 2018 |
| Brain tumor segmentation using dense fully convolutional neural network M Shaikh, G Anand, G Acharya, A Amrutkar, V Alex, G Krishnamurthi International MICCAI brainlesion workshop, 309-319, 2017 | 54 | 2017 |
| Multi-modal brain tumor segmentation using stacked denoising autoencoders K Vaidhya, S Thirunavukkarasu, V Alex, G Krishnamurthi BrainLes 2015, 181-194, 2015 | 53 | 2015 |
| Hypothesis validation of far-wall brightness in carotid-artery ultrasound for feature-based IMT measurement using a combination of level-set segmentation and registration F Molinari, G Krishnamurthi, UR Acharya, SV Sree, G Zeng, L Saba, ... IEEE Transactions on Instrumentation and measurement 61 (4), 1054-1063, 2012 | 51 | 2012 |
| Medical image retrieval using Resnet-18 S Ayyachamy, V Alex, M Khened, G Krishnamurthi Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and …, 2019 | 45 | 2019 |
| Segmentation and classification in digital pathology for glioma research: challenges and deep learning approaches T Kurc, S Bakas, X Ren, A Bagari, A Momeni, Y Huang, L Zhang, A Kumar, ... Frontiers in neuroscience 14, 27, 2020 | 41 | 2020 |
| Prostate tissue characterization/classification in 144 patient population using wavelet and higher order spectra features from transrectal ultrasound images G Pareek, UR Acharya, SV Sree, G Swapna, R Yantri, RJ Martis, L Saba, ... Technology in cancer research & treatment 12 (6), 545-557, 2013 | 41 | 2013 |
| Atheromatic™: Symptomatic vs. asymptomatic classification of carotid ultrasound plaque using a combination of HOS, DWT & texture UR Acharya, O Faust, SV Sree, APC Alvin, G Krishnamurthi, J Sanches, ... 2011 Annual International Conference of the IEEE Engineering in Medicine and …, 2011 | 38 | 2011 |
| Demystifying brain tumor segmentation networks: interpretability and uncertainty analysis P Natekar, A Kori, G Krishnamurthi Frontiers in computational neuroscience 14, 6, 2020 | 32 | 2020 |
| Longitudinal multiple sclerosis lesion segmentation using 3D convolutional neural networks S Vaidya, A Chunduru, R Muthuganapathy, G Krishnamurthi Proceedings of the 2015 longitudinal multiple sclerosis lesion segmentation …, 2015 | 32 | 2015 |
| A generalized deep learning framework for whole-slide image segmentation and analysis M Khened, A Kori, H Rajkumar, G Krishnamurthi, B Srinivasan Scientific reports 11 (1), 1-14, 2021 | 31 | 2021 |
Shravan HanasogeTata Institute of Fundamental ResearchVerified email at tifr.res.in
