831 citations · 976 across the 13 of their papers we have counts for
13 papers
Few-Shot Concept Prompt Learning for Segmentation Foundation Models via Visual Grounding
Rahul Venkataramani, Rachana Sathish
Promptable segmentation foundation models (FMs) such as SAM3 and Medical SAM3 promise few-shot, interactively-specified segmentation for medical imaging through a natural language…
Synthetic Simplicity: Unveiling Bias in Medical Data Augmentation
Krishan Agyakari Raja Babu, Rachana Sathish, Mrunal Pattanaik +1
Synthetic data is becoming increasingly integral in data-scarce fields such as medical imaging, serving as a substitute for real data. However, its inherent statistical characteris…
Task-driven Prompt Evolution for Foundation Models
Rachana Sathish, Rahul Venkataramani, K S Shriram +1
Promptable foundation models, particularly Segment Anything Model (SAM), have emerged as a promising alternative to the traditional task-specific supervised learning for image segm…
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
Chinedu Innocent Nwoye, Tong Yu, Saurav Sharma +46
Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. Th…
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu +59
Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works…
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…
Multitask Learning of Temporal Connectionism in Convolutional Networks using a Joint Distribution Loss Function to Simultaneously Identify Tools and Phase in Surgical Videos
Shanka Subhra Mondal, Rachana Sathish, Debdoot Sheet
Surgical workflow analysis is of importance for understanding onset and persistence of surgical phases and individual tool usage across surgery and in each phase. It is beneficial…
Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy
Oindrila Saha, Rachana Sathish, Debdoot Sheet
Diabetic retinopathy (DR) is the most common form of diabetic eye disease. Retinopathy can affect all diabetic patients and becomes particularly dangerous, increasing the risk of b…