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
this https URL

提出了一个新颖的学习框架，利用多模态经直肠超声引导前列腺癌临床显著性分类，使用两个独立的 3D ResNet-50 从 B 模式和切变波弹性成像 (SWE) 中提取独特特征，同时加入注意模块以精确优化 B 模式特征并聚合两种模态提取的特征。此外，通过使用少样本分割任务增强分类编码器的能力，采用原型修正模块提取临床显著性前列腺癌代表性原型，针对 512 个经过活组织检查证实的前列腺癌 TRUS 视频进行了性能评估，结果表明了准确识别临床显著性前列腺癌的强大能力，达到了 0.86 的曲线下面积（AUC）。此外，该框架生成了视觉类别激活映射（CAM），可用于定位临床显著性前列腺癌，提高生物检查程序的效果。

面向经直肠超声诊断中临床重要前列腺癌分类的多模态融合与基于原型的特征细化

Towards Multi-modality Fusion and Prototype-based Feature Refinement for  Clinically Significant Prostate Cancer Classification in Transrectal  Ultrasound