- D. Jha. “Machine Learning-based Classification, Detection, and Segmentation of Medical Images” PhD Thesis, 2022.
- D. Jha et al., “A Comprehensive analysis of classification methods in gastrointestinal endoscopy imaging,” Medical Image Analysis, vol. 70, 2021.
- D. Jha et al., “A Comprehensive Study on Colorectal Polyp segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation,” IEEE Journal of Biomedical and Health Informatics, 2021.
- D. Jha et al., “ Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning,” IEEE Access, vol. 9, pp. 40496–40510, 2021.
- S. Ali, D. Jha et al., “PolypGen: A multi-center polyp detection and segmentation dataset for generalisability assessment” Nature Scientific Data, 2023.
- H. Borgli*, V. Thambawita*, P. Smedsrud*, S. Hicks*, D. Jha*, S. Eskeland, et al., “Hyper-Kvasir: A Comprehensive Multi-Class Image and Video Dataset for Gastrointestinal Endoscopy,” Nature Scientific Data [*Contributed equally], 2020.
- P. Smedsrud*, H. Gjestang*, O. Nedrejord*, E. Nss*, V. Thambawita*, S. Hicks*, HBorgli*, D. Jha*, et al., Kvasir-Capsule, a video capsule endoscopy dataset, Nature Scientific Data, [*equally contributed], 2021.
- N. Tomar, D. Jha et al., “FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation,” IEEE Transactions on Neural Networks and Learning Systems, 2022.
- A Srivastava, D. Jha et al,”MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation,” IEEE Journal of Biomedical and Health Informatics, 2022.
- T. Ross, A. Reinke, ……D. Jha et al. “Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 Challenge, Medical Image Analysis, vol. 70, 2021.
- D. Jha et al., “Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network,” Journal of Healthcare Engineering, vol. 2017, 13 pages, 2017.
- V. Thambawita, D. Jha et al, “An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning Applied to Gastrointestinal Tract Abnormality Classification , ” ACM Transaction on Computing for Healthcare, vol. 1, no. 3, 2021.
- R. Khadka, D. Jha et al., “Meta-learning with implicit gradients in a few-shot setting for medical image segmentation“, Computers in Biology and Medicine, 2022.
- D. Jha et al., “DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation,” Proceedings of Computer Based Medical System (CBMS), 2020.
- D. Jha et al., “Kvasir-SEG: A segmented polyp dataset,” Proceedings of International Conference on Multimedia Modeling (MMM), pp. 451-462, 2020.
- D. Jha et al., “ResUNet++: An advanced architecture for medical image segmentation,” Proceedings of IEEE International Symposium on Multimedia (ISM 2019), pp. 225-230, 2019.
- D. Jha et al., “ NanoNet: Real-Time Polyp Segmentation in VideoCapsule Endoscopy and Colonoscopy,” Proceedings of IEEE Computer Based Medical System (CBMS), IEEE, 2021.
- D. Jha, et al., “LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification,” Proceedings of The Joint International Conference PDCAT-PAAP2020, Springer, 2020.
- S. A. Hicks, D. Jha, V. Thambawita, P. Halvorsen and M. Riegler, “The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and InferenceTime for Endoscopy,” Proceedings of ICPR workshop, 2020.
- GP. Ji, YC. Chou, DP. Fan, G. Chen, H. Fu, D. Jha, and L. Shao, Progressively Normalized Self-Attention Network for Video Polyp Segmentation, Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI ), 2021.
- N. K Tomar, D. Jha, U. Bagci, Sharib Ali, Text-guided attention for improved polyp segmentation, Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI ), 2022.
- D. Jha et al., “Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy,” IEEE BHI, 2021.
Work on Gastrointestinal tract disease classification
- An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning Applied to Gastrointestinal Tract Abnormality Classification
- The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning
- The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy
Automatic polyp segmentation
- A comprehensive study on colorectal polyp segmentation with ResUNet++, conditional random field and test-time augmentation
Real-time polyp segmentation
Automatic Polyp Detection and Localization
Surgical instrument segmentation
- Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge