7 citations · 8 across the 5 of their papers we have counts for
6 papers
THEMIS: Unlocking Pretrained Knowledge with Foundation Model Embeddings for Anomaly Detection in Time Series
Yadav Mahesh Lorik, Kaushik Sarveswaran, Nagaraj Sundaramahalingam +1
Time series anomaly detection forms a very crucial area in several domains but poses substantial challenges. Due to time series data possessing seasonality, trends, noise, and evol…
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow
Balamurali Murugesan, Sricharan Vijayarangan, Kaushik Sarveswaran +2
Deep learning networks are being developed in every stage of the MRI workflow and have provided state-of-the-art results. However, this has come at the cost of increased computatio…
A context based deep learning approach for unbalanced medical image segmentation
Balamurali Murugesan, Kaushik Sarveswaran, Vijaya Raghavan S +3
Automated medical image segmentation is an important step in many medical procedures. Recently, deep learning networks have been widely used for various medical image segmentation…
Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction
Balamurali Murugesan, Vijaya Raghavan S, Kaushik Sarveswaran +2
Magnetic resonance imaging (MRI) is one of the best medical imaging modalities as it offers excellent spatial resolution and soft-tissue contrast. But, the usage of MRI is limited…
Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation
Balamurali Murugesan, Kaushik Sarveswaran, Sharath M Shankaranarayana +3
For the task of medical image segmentation, fully convolutional network (FCN) based architectures have been extensively used with various modifications. A rising trend in these arc…
Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation
Balamurali Murugesan, Kaushik Sarveswaran, Sharath M Shankaranarayana +2
Image segmentation is a primary task in many medical applications. Recently, many deep networks derived from U-Net have been extensively used in various medical image segmentation…