bibtext: @InProceedings{ Hun_CrossSlice_MICCAI2024,
                   author = { Hung, Alex Ling Yu and Zheng, Haoxin and Zhao, Kai and Pang, Kaifeng and Terzopoulos, Demetri and Sung, Kyunghyun },
                   title = { { Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: Current deep learning-based models typically analyze medical images in either 2D or 3D albeit disregarding volumetric information or suffering sub-optimal performance due to the anisotropic resolution of MR data. Furthermore, providing an accurate uncertainty estimation is beneficial to clinicians, as it indicates how confident a model is about its prediction. We propose a novel 2.5D cross-slice attention model that utilizes both global and local information, along with an evidential critical loss, to perform evidential deep learning for the detection in MR images of prostate cancer, one of the most common cancers and a leading cause of cancer-related death in men. We perform extensive experiments with our model on two different datasets and achieve state-of-the-art performance in prostate cancer detection along with improved epistemic uncertainty estimation. The implementation of the model is available at https://github.com/aL3x-O-o-Hung/GLCSA_ECLoss.
title: Cross-Slice Attention and Evidential Critical Loss for Uncertainty-Aware Prostate Cancer Detection
authors: Hung, Alex Ling Yu Zheng, Haoxin Zhao, Kai Pang, Kaifeng Terzopoulos, Demetri Sung, Kyunghyun