| Dermoscopic image segmentation via multistage fully convolutional networks L Bi, J Kim, E Ahn, A Kumar, M Fulham, D Feng IEEE Transactions on Biomedical Engineering 64 (9), 2065-2074, 2017 | 128 | 2017 |
| Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks L Bi, J Kim, E Ahn, D Feng arXiv preprint arXiv:1703.04197, 2017 | 122 | 2017 |
| Saliency-based lesion segmentation via background detection in dermoscopic images E Ahn, J Kim, L Bi, A Kumar, C Li, M Fulham, DD Feng IEEE journal of biomedical and health informatics 21 (6), 1685-1693, 2017 | 82 | 2017 |
| Automated skin lesion segmentation via image-wise supervised learning and multi-scale superpixel based cellular automata L Bi, J Kim, E Ahn, D Feng, M Fulham 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 1059-1062, 2016 | 46 | 2016 |
| Automated saliency-based lesion segmentation in dermoscopic images E Ahn, L Bi, YH Jung, J Kim, C Li, M Fulham, DD Feng 2015 37th annual international conference of the IEEE engineering in …, 2015 | 39 | 2015 |
| Step-wise integration of deep class-specific learning for dermoscopic image segmentation L Bi, J Kim, E Ahn, A Kumar, D Feng, M Fulham Pattern recognition 85, 78-89, 2019 | 38 | 2019 |
| Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification L Bi, J Kim, E Ahn, D Feng, M Fulham 2016 IEEE 13th international symposium on biomedical imaging (ISBI), 1055-1058, 2016 | 37 | 2016 |
| X-ray image classification using domain transferred convolutional neural networks and local sparse spatial pyramid E Ahn, A Kumar, J Kim, C Li, D Feng, M Fulham 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 855-858, 2016 | 21 | 2016 |
| Semi-automatic skin lesion segmentation via fully convolutional networks L Bi, J Kim, E Ahn, D Feng, M Fulham 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017 | 16 | 2017 |
| Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis E Ahn, A Kumar, M Fulham, D Feng, J Kim Medical image analysis, 2019 | 12 | 2019 |
| Unsupervised deep transfer feature learning for medical image classification E Ahn, A Kumar, D Feng, M Fulham, J Kim 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019 | 7 | 2019 |
| Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification E Ahn, A Kumar, D Feng, M Fulham, J Kim arXiv preprint arXiv:1906.03359, 2019 | 4 | 2019 |
| Automated Saliency-based Melanoma Detection in Dermoscopic Images E Ahn, YH Jung, J Kim Research Conver-sazione 2014, 2014 | 3 | 2014 |
| Development of a risk predictive scoring system to identify patients at risk of representation to emergency department: a retrospective population-based analysis in Australia E Ahn, J Kim, K Rahman, T Baldacchino, C Baird BMJ open 8 (9), e021323, 2018 | 2 | 2018 |
| Unsupervised Deep Feature Learning for Medical Image Analysis E Ahn University of Sydney, 2020 | | 2020 |
| A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image Y Guo, L Bi, E Ahn, D Feng, Q Wang, J Kim Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2020 | | 2020 |
| Unsupervised Domain Adaptation to Classify Medical Images using Zero-bias Convolutional Auto-encoders and Context-based Feature Augmentation E Ahn, A Kumar, M Fulham, D Feng, J Kim IEEE Transactions on Medical Imaging, 2020 | | 2020 |
Jinman KimSchool of Computer Science, University of SydneyVerified email at sydney.edu.au
