| 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 | 1770 | 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 | 1423 | 2018 |
| The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680, 2023 | 933 | 2023 |
| 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 | 344 | 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 | 315 | 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 | 130 | 2012 |
| 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), 11579, 2021 | 124 | 2021 |
| 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 Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS …, 2018 | 113 | 2018 |
| Generative adversarial networks for brain lesion detection V Alex, MS KP, SS Chennamsetty, G Krishnamurthi Medical Imaging 2017: Image Processing 10133, 113-121, 2017 | 97 | 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), 041311-041311, 2017 | 88 | 2017 |
| PAIP 2019: Liver cancer segmentation challenge YJ Kim, H Jang, K Lee, S Park, SG Min, C Hong, JH Park, K Lee, J Kim, ... Medical image analysis 67, 101854, 2021 | 85 | 2021 |
| Medical image retrieval using Resnet-18 S Ayyachamy, V Alex, M Khened, G Krishnamurthi Medical imaging 2019: imaging informatics for healthcare, research, and …, 2019 | 83 | 2019 |
| 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 | 83 | 2013 |
| Demystifying brain tumor segmentation networks: interpretability and uncertainty analysis P Natekar, A Kori, G Krishnamurthi Frontiers in computational neuroscience 14, 6, 2020 | 80 | 2020 |
| Brain tumor segmentation using dense fully convolutional neural network M Shaikh, G Anand, G Acharya, A Amrutkar, V Alex, G Krishnamurthi Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018 | 76 | 2018 |
| 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 | 70 | 2020 |
| 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 | 70 | 2018 |
| 2018 robotic scene segmentation challenge M Allan, S Kondo, S Bodenstedt, S Leger, R Kadkhodamohammadi, ... arXiv preprint arXiv:2001.11190, 2020 | 69 | 2020 |
| Multi-modal brain tumor segmentation using stacked denoising autoencoders K Vaidhya, S Thirunavukkarasu, V Alex, G Krishnamurthi Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2016 | 65 | 2016 |
| 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 | 60 | 2012 |
Shravan HanasogeTata Institute of Fundamental ResearchVerified email at tifr.res.in
