118 citations · 323 across the 22 of their papers we have counts for
24 papers
MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring
Shrish Shrinath Vaidya, Minmini Selvam, Ganapathy Krishnamurthi
The adoption of Large Language Models for radiology report structuring has been largely dominated by either proprietary cloud-based models or large open models requiring high-end G…
MedPAO-VL: Interpretable and Protocol-Compliant Agent for Structured Radiology Report Generation
Shrish Shrinath Vaidya, Gowthamaan Palani, Minmini Selvam +2
Despite the remarkable generalization capabilities of Large Language Models, generating clinically accurate and structured radiology reports from medical images remains a significa…
Diffusion Autoencoder for Unsupervised Artifact Restoration in Handheld Fundus Images
Mathumetha Palani, Kavya Puthumana, Ayantika Das +1
The advent of handheld fundus imaging devices has made ophthalmologic diagnosis and disease screening more accessible, efficient, and cost-effective. However, images captured from…
Exact Constraint Enforcement in Physics-Informed Extreme Learning Machines using Null-Space Projection Framework
Rishi Mishra, Smriti, Balaji Srinivasan +2
Physics-informed extreme learning machines (PIELMs) typically impose boundary and initial conditions through penalty terms, yielding only approximate satisfaction that is sensitive…
MedPAO: A Protocol-Driven Agent for Structuring Medical Reports
Shrish Shrinath Vaidya, Gowthamaan Palani, Sidharth Ramesh +4
The deployment of Large Language Models (LLMs) for structuring clinical data is critically hindered by their tendency to hallucinate facts and their inability to follow domain-spec…
Eig-PIELM: A Mesh-Free Approach for Efficient Eigen-Analysis with Physics-Informed Extreme Learning Machines
Rishi Mishra, Smriti, Ganapathy Krishnamurthi +2
In this work, a novel Eig-PIELM framework is proposed that extends physics-informed extreme learning machine for an efficient and accurate solution of linear eigenvalue problems. T…
Efficient Knowledge Distillation of SAM for Medical Image Segmentation
Kunal Dasharath Patil, Gowthamaan Palani, Ganapathy Krishnamurthi
The Segment Anything Model (SAM) has set a new standard in interactive image segmentation, offering robust performance across various tasks. However, its significant computational…
PICS in Pics: Physics Informed Contour Selection for Rapid Image Segmentation
Vikas Dwivedi, Balaji Srinivasan, Ganapathy Krishnamurthi
Effective training of deep image segmentation models is challenging due to the need for abundant, high-quality annotations. Generating annotations is laborious and time-consuming f…
PARSE challenge 2022: Pulmonary Arteries Segmentation using Swin U-Net Transformer(Swin UNETR) and U-Net
Akansh Maurya, Kunal Dashrath Patil, Rohan Padhy +2
In this work, we present our proposed method to segment the pulmonary arteries from the CT scans using Swin UNETR and U-Net-based deep neural network architecture. Six models, thre…
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…
Abstracting Deep Neural Networks into Concept Graphs for Concept Level Interpretability
Avinash Kori, Parth Natekar, Ganapathy Krishnamurthi +1
The black-box nature of deep learning models prevents them from being completely trusted in domains like biomedicine. Most explainability techniques do not capture the concept-base…
Distangling Biological Noise in Cellular Images with a focus on Explainability
Manik Sharma, Ganapathy Krishnamurthi
The cost of some drugs and medical treatments has risen in recent years that many patients are having to go without. A classification project could make researchers more efficient.…
Solar wind prediction using deep learning
Vishal Upendran, M. C. M Cheung, Shravan Hanasoge +1
Emanating from the base of the Sun's corona, the solar wind fills the interplanetary medium with a magnetized stream of charged particles whose interaction with the Earth's magneto…
Structurally aware bidirectional unpaired image to image translation between CT and MR
Vismay Agrawal, Avinash Kori, Vikas Kumar Anand +1
Magnetic Resonance (MR) Imaging and Computed Tomography (CT) are the primary diagnostic imaging modalities quite frequently used for surgical planning and analysis. A general probl…
2018 Robotic Scene Segmentation Challenge
Max Allan, Satoshi Kondo, Sebastian Bodenstedt +38
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward ki…
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…
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis
Parth Natekar, Avinash Kori, Ganapathy Krishnamurthi
The accurate automatic segmentation of gliomas and its intra-tumoral structures is important not only for treatment planning but also for follow-up evaluations. Several methods bas…
Enhanced Optic Disk and Cup Segmentation with Glaucoma Screening from Fundus Images using Position encoded CNNs
Vismay Agrawal, Avinash Kori, Varghese Alex +1
In this manuscript, we present a robust method for glaucoma screening from fundus images using an ensemble of convolutional neural networks (CNNs). The pipeline comprises of first…
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…
Enhanced Image Classification With Data Augmentation Using Position Coordinates
Avinash Kori, Ganapathy Krishnamurthi, Balaji Srinivasan
In this paper we propose the use of image pixel position coordinate system to improve image classification accuracy in various applications. Specifically, we hypothesize that the u…
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…
Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis
Varghese Alex, Mohammed Safwan, Ganapathy Krishnamurthi
In this paper, we use a fully convolutional neural network (FCNN) for the segmentation of gliomas from Magnetic Resonance Images (MRI). A fully automatic, voxel based classificatio…
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation
Varghese Alex, Kiran Vaidhya, Subramaniam Thirunavukkarasu +2
The work presented explores the use of denoising autoencoders (DAE) for brain lesion detection, segmentation and false positive reduction. Stacked denoising autoencoders (SDAE) wer…
Asteroseismic determination of fundamental parameters of sun-like stars using multi-layered neural networks
Kuldeep Verma, Shravan Hanasoge, Jishnu Bhattacharya +2
The advent of space-based observatories such as CoRoT and Kepler has enabled the testing of our understanding of stellar evolution on thousands of stars. Evolutionary models typica…