Prostate cancer is the most common noncutaneous cancer in the world.
Recently, multi-modality transrectal ultrasound (TRUS) has increasingly become
an effective tool for the guidance of prostate biopsies. With the aim of
effectively identifying prostate cancer, we propose a framework for the
classification of clinically significant prostate cancer (csPCa) from
multi-modality TRUS videos. The framework utilizes two 3D ResNet-50 models to
extract features from B-mode images and shear wave elastography images,
respectively. An adaptive spatial fusion module is introduced to aggregate two
modalities' features. An orthogonal regularized loss is further used to
mitigate feature redundancy. The proposed framework is evaluated on an in-house
dataset containing 512 TRUS videos, and achieves favorable performance in
identifying csPCa with an area under curve (AUC) of 0.84. Furthermore, the
visualized class activation mapping (CAM) images generated from the proposed
framework may provide valuable guidance for the localization of csPCa, thus
facilitating the TRUS-guided targeted biopsy. Our code is publicly available at
this https URL

为了有效识别临床显著性前列腺癌 (csPCa)，我们提出了一个利用多模态经直肠超声 (TRUS) 视频进行临床显著性前列腺癌的分类的框架。该框架利用两个 3D ResNet-50 模型从 B 模式图像和剪切波弹性成像图像中提取特征。引入了自适应空间融合模块来聚集两种模态的特征，同时使用正交正则化损失来减轻特征的冗余。在包含 512 个 TRUS 视频的内部数据集上对该框架进行评估，其在识别 csPCa 方面取得了有利的表现，AUC 为 0.84。此外，从该框架生成的视觉类激活映射 (CAM) 图像可能为 csPCa 的定位提供有价值的指导，从而促进 TRUS 引导的靶向活检。我们的代码公开在此 https 网址。