Hang Min
Hang Min
Ingham Institute, CSIRO e-health
Verified email at csiro.au
Cited by
Cited by
A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation
J Chu, H Min, L Liu, W Lu
Medical physics 42 (7), 3859-3869, 2015
Multi-scale mass segmentation for mammograms via cascaded random forests
H Min, SS Chandra, N Dhungel, S Crozier, AP Bradley
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017
Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN
H Min, D Wilson, Y Huang, L Siyu, S Crozier, AP Bradley, SS Chandra
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 1111-1115, 2020
Multi-scale sifting for mammographic mass detection and segmentation
H Min, SS Chandra, S Crozier, AP Bradley
Biomedical Physics & Engineering Express 5 (2), 025022, 2019
A review of medical image data augmentation techniques for deep learning applications
P Chlap, H Min, N Vandenberg, J Dowling, L Holloway, A Haworth
Journal of Medical Imaging and Radiation Oncology, 2021
Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI
H Min, D McClymont, SS Chandra, S Crozier, AP Bradley
Biomedical Physics & Engineering Express 6 (6), 065027, 2020
Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial
H Min, J Dowling, MG Jameson, K Cloak, J Faustino, M Sidhom, J Martin, ...
Physics in Medicine & Biology 66 (19), 195008, 2021
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