29 citations · 123 across the 24 of their papers we have counts for
25 papers
When CNNs Outperform Transformers and Mambas: Revisiting Deep Architectures for Dental Caries Segmentation
Aashish Ghimire, Jun Zeng, Roshan Paudel +6
Accurate identification and segmentation of dental caries in panoramic radiographs are critical for early diagnosis and effective treatment planning. Automated segmentation remains…
SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes
Jun Zeng, Quoc-Huy Trinh, Deepak Ranjan Nayak +3
Liver cirrhosis plays a critical role in the prognosis of chronic liver disease. Early detection and timely intervention are essential for reducing mortality rates. However, the in…
PolypDB: A Curated Multi-Center Dataset for Development of AI Algorithms in Colonoscopy
Debesh Jha, Nikhil Kumar Tomar, Vanshali Sharma +19
Colonoscopy is the primary method for examination, detection, and removal of polyps. However, challenges such as variations among the endoscopists' skills, bowel quality preparatio…
Transformer-Enhanced Iterative Feedback Mechanism for Polyp Segmentation
Nikhil Kumar Tomar, Debesh Jha, Koushik Biswas +4
Colorectal cancer (CRC) is the third most common cause of cancer diagnosed in the United States and the second leading cause of cancer-related death among both genders. Notably, CR…
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation
Debesh Jha, Nikhil Kumar Tomar, Koushik Biswas +11
Accurate segmentation of organs from abdominal CT scans is essential for clinical applications such as diagnosis, treatment planning, and patient monitoring. To handle challenges o…
CT Liver Segmentation via PVT-based Encoding and Refined Decoding
Debesh Jha, Nikhil Kumar Tomar, Koushik Biswas +7
Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning. Computer-aided diagnosis systems promise to improve the precision of liver di…
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans
Koushik Biswas, Debesh Jha, Nikhil Kumar Tomar +7
In this study, we propose a new activation function, called Adaptive Smooth Activation Unit (ASAU), tailored for optimized gradient propagation, thereby enhancing the proficiency o…
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection
Debesh Jha, Vanshali Sharma, Neethi Dasu +8
Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance. These challenges include data availability, bi…
Prototype Learning for Out-of-Distribution Polyp Segmentation
Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci
Existing polyp segmentation models from colonoscopy images often fail to provide reliable segmentation results on datasets from different centers, limiting their applicability. Our…
Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges
Debesh Jha, Vanshali Sharma, Debapriya Banik +30
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps durin…
TransRUPNet for Improved Polyp Segmentation
Debesh Jha, Nikhil Kumar Tomar, Debayan Bhattacharya +1
Colorectal cancer is among the most common cause of cancer worldwide. Removal of precancerous polyps through early detection is essential to prevent them from progressing to colon…
A Conceptual Algorithm for Applying Ethical Principles of AI to Medical Practice
Debesh Jha, Gorkem Durak, Vanshali Sharma +12
Artificial Intelligence (AI) is poised to transform healthcare delivery through revolutionary advances in clinical decision support and diagnostic capabilities. While human experti…
TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing
Debesh Jha, Nikhil Kumar Tomar, Vanshali Sharma +1
Colonoscopy is considered the most effective screening test to detect colorectal cancer (CRC) and its precursor lesions, i.e., polyps. However, the procedure experiences high miss…
RUPNet: Residual upsampling network for real-time polyp segmentation
Nikhil Kumar Tomar, Ulas Bagci, Debesh Jha
Colorectal cancer is among the most prevalent cause of cancer-related mortality worldwide. Detection and removal of polyps at an early stage can help reduce mortality and even help…
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…
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…
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…
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…
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Sharib Ali, Noha Ghatwary, Debesh Jha +29
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and charac…
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
Debesh Jha, Nikhil Kumar Tomar, Sharib Ali +5
Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation metho…
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
Nikhil Kumar Tomar, Debesh Jha, Michael A. Riegler +5
The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Imag…
Automatic Polyp Segmentation using Fully Convolutional Neural Network
Nikhil Kumar Tomar
Colorectal cancer is one of fatal cancer worldwide. Colonoscopy is the standard treatment for examination, localization, and removal of colorectal polyps. However, it has been show…
Automatic Polyp Segmentation using U-Net-ResNet50
Saruar Alam, Nikhil Kumar Tomar, Aarati Thakur +2
Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the…