Vandan Gorade
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View article: Large Scale MRI Collection and Segmentation of Cirrhotic Liver
Large Scale MRI Collection and Segmentation of Cirrhotic Liver Open
Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly increases mortality risk. While magnetic resonance imaging (MRI) offers a non-invasive asses…
View article: L2GNet: Optimal Local-to-Global Representation of Anatomical Structures for Generalized Medical Image Segmentation
L2GNet: Optimal Local-to-Global Representation of Anatomical Structures for Generalized Medical Image Segmentation Open
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 Discrete Latent Space (CDLS) models show promise in handli…
View article: Large-scale multi-center CT and MRI segmentation of pancreas with deep learning
Large-scale multi-center CT and MRI segmentation of pancreas with deep learning Open
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are under…
View article: Large Scale MRI Collection and Segmentation of Cirrhotic Liver
Large Scale MRI Collection and Segmentation of Cirrhotic Liver Open
Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly increases mortality risk. While magnetic resonance imaging (MRI) offers a non-invasive asses…
View article: Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs
Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs Open
Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning. Existing segmentation models often fail to capture complex feature interactions and genera…
View article: Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning
Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning Open
Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are under…
View article: OTCXR: Rethinking Self-supervised Alignment using Optimal Transport for Chest X-ray Analysis
OTCXR: Rethinking Self-supervised Alignment using Optimal Transport for Chest X-ray Analysis Open
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, conventional SSL methods face challenges in achieving semantic al…
View article: MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning
MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning Open
Self-supervised learning (SSL) is potentially useful in reducing the need for manual annotation and making deep learning models accessible for medical image analysis tasks. By leveraging the representations learned from unlabeled data, sel…
View article: Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation
Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation Open
Deep learning has demonstrated remarkable achievements in medical image segmentation. However, prevailing deep learning models struggle with poor generalization due to (i) intra-class variations, where the same class appears differently in…
View article: Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation Open
Knowledge distillation (KD) has demonstrated remarkable success across various domains, but its application to medical imaging tasks, such as kidney and liver tumor segmentation, has encountered challenges. Many existing KD methods are not…
View article: SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation Open
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, these models encounter distinct challenges. CLS models cap…
View article: MBGRLp: Multiscale Bootstrap Graph Representation Learning on Pointcloud (Student Abstract)
MBGRLp: Multiscale Bootstrap Graph Representation Learning on Pointcloud (Student Abstract) Open
Point cloud has gained a lot of attention with the availability of a large amount of point cloud data and increasing applications like city planning and self-driving cars. However, current methods, often rely on labeled information and cos…
View article: Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping
Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping Open
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 low and high-frequency features at the same time. Further,…