bibtext: @InProceedings{ Wu_Towards_MICCAI2024,
                   author = { Wu, Hong and Fu, Juan and Ye, Hongsheng and Zhong, Yuming and Zou, Xuebin and Zhou, Jianhua and Wang, Yi },
                   title = { { Towards Multi-modality Fusion and Prototype-based Feature Refinement for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: Prostate cancer is a highly prevalent cancer and ranks as the second leading cause of cancer-related deaths in men globally. Recently, the utilization of multi-modality transrectal ultrasound (TRUS) has gained significant traction as a valuable technique for guiding prostate biopsies. In this study, we present a novel learning framework for clinically significant prostate cancer (csPCa) classification by using multi-modality TRUS. The proposed framework employs two separate 3D ResNet-50 to extract distinctive features from B-mode and shear wave elastography (SWE). Additionally, an attention module is incorporated to effectively refine B-mode features and aggregate the extracted features from both modalities. Furthermore, we utilize few shot segmentation task to enhance the capacity of the classification encoder. Due to the limited availability of csPCa masks, a prototype correction module is employed to extract representative prototypes of csPCa. The performance of the framework is assessed on a large-scale dataset consisting of 512 TRUS videos with biopsy-proved prostate cancer. The results demonstrate the strong capability in accurately identifying csPCa, achieving an area under the curve (AUC) of 0.86. Moreover, the framework generates visual class activation mapping (CAM), which can serve as valuable assistance for localizing csPCa. These CAM images may offer valuable guidance during TRUS-guided targeted biopsies, enhancing the efficacy of the biopsy procedure. The code is available at https://github.com/2313595986/SmileCode.
title: Towards Multi-modality Fusion and Prototype-based Feature Refinement for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound
authors: Wu, Hong Fu, Juan Ye, Hongsheng Zhong, Yuming Zou, Xuebin Zhou, Jianhua Wang, Yi