| 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 | 2101 | 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 | 1853 | 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 | 1299 | 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 | 389 | 2019 |
| 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 | 168 | 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 | 125 | 2018 |
| 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 | 115 | 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 | 100 | 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 | 83 | 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 | 83 | 2018 |
| Brain tumor segmentation and survival prediction using automatic hard mining in 3D CNN architecture VK Anand, S Grampurohit, P Aurangabadkar, A Kori, M Khened, RS Bhat, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2021 | 42 | 2021 |
| Ensemble of fully convolutional neural network for brain tumor segmentation from magnetic resonance images A Kori, M Soni, B Pranjal, M Khened, V Alex, G Krishnamurthi International MICCAI Brainlesion Workshop, 485-496, 2018 | 23 | 2018 |
| A combined radio-histological approach for classification of low grade gliomas A Bagari, A Kumar, A Kori, M Khened, G Krishnamurthi International MICCAI Brainlesion Workshop, 416-427, 2018 | 13 | 2018 |
| Fully automatic segmentation for ischemic stroke using CT perfusion maps VK Anand, M Khened, V Alex, G Krishnamurthi Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019 | 7 | 2019 |
| 3D convolution neural networks for molecular subtype prediction in glioblastoma multiforme M Khened, VK Anand, G Acharya, N Shah, G Krishnamurthi Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and …, 2019 | 4 | 2019 |
| Convolutional neural network for kidney and kidney tumor segmentation VK Anand, P Aurangabadkar, M Khened, G Krishnamurthi Proceedings of 2019 Kidney Tumor Segmentation Challenge: KiTS19, 1-7, 2019 | 3 | 2019 |
| Convolutional Neural Network for Kidney and kidney Tumor segmentation V Kumar Anand, P Aurangabadkar, M Khened, G Krishnamurthi University of Minnesota Libraries Publishing, 2019 | 1 | 2019 |
| 2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation K Chaitanya Kaluva, M Khened, A Kori, G Krishnamurthi arXiv e-prints, arXiv: 1802.02182, 2018 | | 2018 |
| Deep learning based algorithms for image analysis in radiology and digital pathology M KHENED Chennai, 0 | | |
| Ensemble of Deep 2D and 3D Fully Convolutional Neural Network for Brain Tumor Segmentation A Kori, M Soni, B Pranjal, M Khened, V Alex, G Krishnamurthi | | |
Ganapathy KrishnamurthiAssociate Professor at IIT-Madras在 iitm.ac.in 的电子邮件经过验证
Ganapathy KrishnamurthiAssociate Professor at IIT-Madras在 iitm.ac.in 的电子邮件经过验证
varghese alexResearch Expert Siemens Healthineers在 smail.iitm.ac.in 的电子邮件经过验证