13 citations · 17 across the 5 of their papers we have counts for
8 papers
Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss
Rakshith Sathish, Rachana Sathish, Ramanathan Sethuraman +1
Lung cancer is the most common form of cancer found worldwide with a high mortality rate. Early detection of pulmonary nodules by screening with a low-dose computed tomography (CT)…
Identification of Cervical Pathology using Adversarial Neural Networks
Abhilash Nandy, Rachana Sathish, Debdoot Sheet
Various screening and diagnostic methods have led to a large reduction of cervical cancer death rates in developed countries. However, cervical cancer is the leading cause of cance…
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
A. Emre Kavur, N. Sinem Gezer, Mustafa Barış +24
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have intr…
Significance of Residual Learning and Boundary Weighted Loss in Ischaemic Stroke Lesion Segmentation
Ronnie Rajan, Rachana Sathish, Debdoot Sheet
Radiologists use various imaging modalities to aid in different tasks like diagnosis of disease, lesion visualization, surgical planning and prognostic evaluation. Most of these ta…
Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging
Rachana Sathish, Ronnie Rajan, Anusha Vupputuri +2
Ischaemic stroke is a medical condition caused by occlusion of blood supply to the brain tissue thus forming a lesion. A lesion is zoned into a core associated with irreversible ne…
Unit Impulse Response as an Explainer of Redundancy in a Deep Convolutional Neural Network
Rachana Sathish, Debdoot Sheet
Convolutional neural networks (CNN) are generally designed with a heuristic initialization of network architecture and trained for a certain task. This often leads to overparametri…