activity
20222025
most citedLarge Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping

4 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

DiSSECT: Structuring Transfer-Ready Medical Image Representations through Discrete Self-Supervision

Azad Singh, Deepak Mishra

Self-supervised learning (SSL) has emerged as a powerful paradigm for medical image representation learning, particularly in settings with limited labeled data. However, existing S…

eess.IV2025

RobSurv: Vector Quantization-Based Multi-Modal Learning for Robust Cancer Survival Prediction

Aiman Farooq, Azad Singh, Deepak Mishra +1

Cancer survival prediction using multi-modal medical imaging presents a critical challenge in oncology, mainly due to the vulnerability of deep learning models to noise and protoco…

eess.IV2024

RibCageImp: A Deep Learning Framework for 3D Ribcage Implant Generation

Gyanendra Chaubey, Aiman Farooq, Azad Singh +1

The recovery of damaged or resected ribcage structures requires precise, custom-designed implants to restore the integrity and functionality of the thoracic cavity. Traditional imp…

cs.CV2024

CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning

Azad Singh, Deepak Mishra

Self-supervised learning (SSL) has emerged as a promising paradigm for medical image analysis by harnessing unannotated data. Despite their potential, the existing SSL approaches o…

eess.IV2024

MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning

Azad Singh, Vandan Gorade, Deepak Mishra

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 leve…

cs.CV2022★ 4 cited

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…