405 citations · 1.1k across the 81 of their papers we have counts for
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A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications
Tara M. Pattilachan, Ugur Demir, Elif Keles +7
Current data augmentation techniques and transformations are well suited for improving the size and quality of natural image datasets but are not yet optimized for medical imaging.…
Domain Generalization with Correlated Style Uncertainty
Zheyuan Zhang, Bin Wang, Debesh Jha +2
Domain generalization (DG) approaches intend to extract domain invariant features that can lead to a more robust deep learning model. In this regard, style augmentation is a strong…
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation
Abhishek Srivastava, Debesh Jha, Bulent Aydogan +2
Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning. The varying anatomic locations and dimensions of H\&N nodal…
DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation
Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci
Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Excision of polyps during colonoscopy helps reduce mortality and morbidity for CRC. Powered b…
An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation
Abhishek Srivastava, Debesh Jha, Elif Keles +3
Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning. Existing deep learning-based multi-scale fusion architectures have demonst…
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions
Ashish Rauniyar, Desta Haileselassie Hagos, Debesh Jha +4
With the advent of the IoT, AI, ML, and DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnos…