29 citations · 191 across the 76 of their papers we have counts for
15 papers · 1 filter
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…
Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network
Nikhil Kumar Tomar, Abhishek Srivastava, Ulas Bagci +1
The detection and removal of precancerous polyps through colonoscopy is the primary technique for the prevention of colorectal cancer worldwide. However, the miss rate of colorecta…
Video Analytics in Elite Soccer: A Distributed Computing Perspective
Debesh Jha, Ashish Rauniyar, Håvard D. Johansen +4
Ubiquitous sensors and Internet of Things (IoT) technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training,…
TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation
Nikhil Kumar Tomar, Annie Shergill, Brandon Rieders +2
Colorectal cancer (CRC) is one of the most common causes of cancer and cancer-related mortality worldwide. Performing colon cancer screening in a timely fashion is the key to early…
Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network
Abhishek Srivastava, Nikhil Kumar Tomar, Ulas Bagci +1
Video capsule endoscopy is a hot topic in computer vision and medicine. Deep learning can have a positive impact on the future of video capsule endoscopy technology. It can improve…
Dynamic Linear Transformer for 3D Biomedical Image Segmentation
Zheyuan Zhang, Ulas Bagci
Transformer-based neural networks have surpassed promising performance on many biomedical image segmentation tasks due to a better global information modeling from the self-attenti…
Transformer based Generative Adversarial Network for Liver Segmentation
Ugur Demir, Zheyuan Zhang, Bin Wang +6
Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prog…
TGANet: Text-guided attention for improved polyp segmentation
Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci +1
Colonoscopy is a gold standard procedure but is highly operator-dependent. Automated polyp segmentation, a precancerous precursor, can minimize missed rates and timely treatment of…
Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem
Amir Emad Marvasti, Ehsan Emad Marvasti, Ulas Bagci
Maximum Probability Framework, powered by Maximum Probability Theorem, is a recent theoretical development in artificial intelligence, aiming to formally define probabilistic model…
Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks
Ilkin Isler, Curtis Lisle, Justin Rineer +4
Organ at risk (OAR) segmentation is a crucial step for treatment planning and outcome determination in radiotherapy treatments of cancer patients. Several deep learning based segme…