  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 propose a
novel learning framework for clinically significant prostate cancer (csPCa)
classification 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 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.
