4 citations · 14 across the 11 of their papers we have counts for
6 papers · 1 filter
L2GNet: Optimal Local-to-Global Representation of Anatomical Structures for Generalized Medical Image Segmentation
Vandan Gorade, Sparsh Mittal, Neethi Dasu +3
Continuous Latent Space (CLS) and Discrete Latent Space (DLS) models, like AttnUNet and VQUNet, have excelled in medical image segmentation. In contrast, Synergistic Continuous and…
Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs
Vandan Gorade, Onkar Susladkar, Gorkem Durak +7
Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning. Existing segmentation models of…
OTCXR: Rethinking Self-supervised Alignment using Optimal Transport for Chest X-ray Analysis
Vandan Gorade, Azad Singh, Deepak Mishra
Self-supervised learning (SSL) has emerged as a promising technique for analyzing medical modalities such as X-rays due to its ability to learn without annotations. However, conven…
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation
Vandan Gorade, Sparsh Mittal, Debesh Jha +1
Knowledge distillation (KD) has demonstrated remarkable success across various domains, but its application to medical imaging tasks, such as kidney and liver tumor segmentation, h…
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation
Vandan Gorade, Sparsh Mittal, Debesh Jha +1
In recent years, continuous latent space (CLS) and discrete latent space (DLS) deep learning models have been proposed for medical image analysis for improved performance. However,…
Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping
Vandan Gorade, Azad Singh, Deepak Mishra
Learning representation from unlabeled time series data is a challenging problem. Most existing self-supervised and unsupervised approaches in the time-series domain do not capture…