Ashis K. Dhara
According to our database1,
Ashis K. Dhara
authored at least 30 papers
between 2012 and 2024.
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Bibliography
2024
An attention enriched encoder-decoder architecture with CLSTM and RES unit for segmenting exudate in retinal images.
Signal Image Video Process., June, 2024
ASE-Net for Segmentation of Post-Operative Glioblastoma and Patient-Specific Fine-Tuning for Segmentation Refinement of Follow-Up MRI Scans.
SN Comput. Sci., January, 2024
2023
3-D Attention-SEV-Net for Segmentation of Post-operative Glioblastoma with Interactive Correction of Over-Segmentation.
Proceedings of the Pattern Recognition and Machine Intelligence, 2023
Proceedings of the Medical Imaging 2023: Image Processing, 2023
2022
Nested U-Net for Segmentation of Red Lesions in Retinal Fundus Images and Sub-image Classification for Removal of False Positives.
J. Digit. Imaging, 2022
Automatic grading of retinal blood vessel tortuosity using Modified CNN in deep retinal image diagnosis.
Biomed. Signal Process. Control., 2022
Automatic detection and segmentation of optic disc using a modified convolution network.
Biomed. Signal Process. Control., 2022
Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022
2020
Segmentation of Intracranial Aneurysm Remnant in MRA using Dual-Attention Atrous Net.
Proceedings of the 25th International Conference on Pattern Recognition, 2020
2019
Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval.
J. Digit. Imaging, 2019
2018
Segmentation of Lung Field in HRCT Images Using U-Net Based Fully Convolutional Networks.
Proceedings of the Medical Image Understanding and Analysis - 22nd Conference, 2018
Convolutional Neural Networks for Efficient Localization of Interstitial Lung Disease Patterns in HRCT Images.
Proceedings of the Medical Image Understanding and Analysis - 22nd Conference, 2018
Optic Disc Segmentation in Retinal Fundus Images Using Fully Convolutional Network and Removal of False-Positives Based on Shape Features.
Proceedings of the Deep Learning in Medical Image Analysis - and - Multimodal Learning for Clinical Decision Support, 2018
Segmentation of Post-operative Glioblastoma in MRI by U-Net with Patient-Specific Interactive Refinement.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
Proceedings of the 24th International Conference on Pattern Recognition, 2018
2017
Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer.
J. Digit. Imaging, 2017
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017
2016
J. Digit. Imaging, 2016
A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.
J. Digit. Imaging, 2016
IET Image Process., 2016
Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images.
Int. J. Comput. Assist. Radiol. Surg., 2016
An automated lung nodule detection system for CT images using synthetic minority oversampling.
Proceedings of the Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, California, United States, 27 February, 2016
Classification of pulmonary nodules in lung CT images using shape and texture features.
Proceedings of the Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, California, United States, 27 February, 2016
2015
Development of a screening tool for staging of diabetic retinopathy in fundus images.
Proceedings of the Medical Imaging 2015: Computer-Aided Diagnosis, 2015
2014
2013
3D texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images.
Proceedings of the Medical Imaging 2013: Computer-Aided Diagnosis, 2013
Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images.
Proceedings of the Medical Imaging 2013: Computer-Aided Diagnosis, 2013
Automated lung field segmentation in CT images using mean shift clustering and geometrical features.
Proceedings of the Medical Imaging 2013: Computer-Aided Diagnosis, 2013
2012
A hybrid preprocessing method using geometry based diffusion and elective enhancement filtering for pulmonary nodule detection.
Proceedings of the Medical Imaging 2012: Computer-Aided Diagnosis, 2012