CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI

no code implementations • 29 Mar 2022 • Alex Ling Yu Hung, Haoxin Zheng, Qi Miao, Steven S. Raman, Demetri Terzopoulos, Kyunghyun Sung

However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices.

Automatic Prostate Zonal Segmentation Using Fully Convolutional Network with Feature Pyramid Attention

no code implementations • 31 Oct 2019 • Yongkai Liu, Guang Yang, Sohrab Afshari Mirak, Melina Hosseiny, Afshin Azadikhah, Xinran Zhong, Robert E. Reiter, Yeejin Lee, Steven Raman, Kyunghyun Sung

Expert 1) were 0. 71 (PZ) and 0. 75 (TZ).

Deep Learning-based Radiomic Features for Improving Neoadjuvant Chemoradiation Response Prediction in Locally Advanced Rectal Cancer

no code implementations • 9 Sep 2019 • Jie Fu, Xinran Zhong, Ning li, Ritchell Van Dams, John Lewis, Kyunghyun Sung, Ann C. Raldow, Jing Jin, X. Sharon Qi

The model built with handcrafted features achieved the mean area under the ROC curve (AUC) of 0. 64, while the one built with DL-based features yielded the mean AUC of 0. 73.

Survival Prediction