67 citations · 67 across the 4 of their papers we have counts for
4 papers
Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture
Vikas Kumar Anand, Sanjeev Grampurohit, Pranav Aurangabadkar +4
We utilize 3-D fully convolutional neural networks (CNN) to segment gliomas and its constituents from multimodal Magnetic Resonance Images (MRI). The architecture uses dense connec…
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis
Mahendra Khened, Avinash Kori, Haran Rajkumar +2
Histopathology tissue analysis is considered the gold standard in cancer diagnosis and prognosis. Given the large size of these images and the increase in the number of potential c…
Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers
Mahendra Khened, Varghese Alex Kollerathu, Ganapathy Krishnamurthi
Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of par…
2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation
Krishna Chaitanya Kaluva, Mahendra Khened, Avinash Kori +1
In this paper we propose a fully automatic 2-stage cascaded approach for segmentation of liver and its tumors in CT (Computed Tomography) images using densely connected fully convo…