29 citations · 191 across the 76 of their papers we have counts for
21 papers · 1 filter
PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-Identification
Quoc-Huy Trinh, Nhat-Tan Bui, Dinh-Hieu Hoang +6
Person Re-Identification (Re-ID) task seeks to enhance the tracking of multiple individuals by surveillance cameras. It supports multimodal tasks, including text-based person retri…
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…
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation
Vandan Gorade, Sparsh Mittal, Debesh Jha +1
Knowledge distillation (KD) has demonstrated remarkable success across various domains, but its application to medical imaging tasks, such as kidney and liver tumor segmentation, h…
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation
Vandan Gorade, Sparsh Mittal, Debesh Jha +1
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,…
DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
Zheyuan Zhang, Lanhong Yao, Bin Wang +5
Large-scale, big-variant, high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better gene…
Radiomics Boosts Deep Learning Model for IPMN Classification
Lanhong Yao, Zheyuan Zhang, Ugur Demir +14
Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their ri…
Domain Generalization with Fourier Transform and Soft Thresholding
Hongyi Pan, Bin Wang, Zheyuan Zhang +5
Domain generalization aims to train models on multiple source domains so that they can generalize well to unseen target domains. Among many domain generalization methods, Fourier-t…
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…
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation
Lanhong Yao, Zheyuan Zhang, Ulas Bagci
Brain tumor segmentation is an active research area due to the difficulty in delineating highly complex shaped and textured tumors as well as the failure of the commonly used U-Net…
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…
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification
Ilkin Isler, Debesh Jha, Curtis Lisle +6
In this study, our goal is to show the impact of self-supervised pre-training of transformers for organ at risk (OAR) and tumor segmentation as compared to costly fully-supervised…
GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
Bin Wang, Hongyi Pan, Armstrong Aboah +7
Eye tracking research is important in computer vision because it can help us understand how humans interact with the visual world. Specifically for high-risk applications, such as…
GazeSAM: What You See is What You Segment
Bin Wang, Armstrong Aboah, Zheyuan Zhang +1
This study investigates the potential of eye-tracking technology and the Segment Anything Model (SAM) to design a collaborative human-computer interaction system that automates med…
Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classification
Smriti Regmi, Aliza Subedi, Ulas Bagci +1
Medical image analysis is a hot research topic because of its usefulness in different clinical applications, such as early disease diagnosis and treatment. Convolutional neural net…
Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation
Zheyuan Zhang, Bin Wang, Lanhong Yao +5
Most statistical learning algorithms rely on an over-simplified assumption, that is, the train and test data are independent and identically distributed. In real-world scenarios, h…
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…
The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence
Elif Keles, Ulas Bagci
Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent develop…
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…