The 50 slides of dataset and trained models

Binary semantic segmentation for detection of prostate adenocarcinoma using an ensemble with attention and residual U-Net architecturesAn accurate determination of the Gleason Score or Gleason Pattern (GP) is crucial in the diagnosis of prostate cancer (PCa) because it is one of the criterion used to guide treatment decisions for prognostic-risk groups. However, the manually designation of GP by a pathologist using a microscope is prone to error and subject to significant inter-observer variability. Deep learning has been used to automatically differentiate GP on digitized slides, aiding pathologists and reducing inter-observer variability, especially in the early GP of cancer. This paper presents a binary semantic segmentation for the GP of prostate adenocarcinoma. The segmentation separates benign and malignant tissues, with the malignant class consisting of adenocarcinoma GP3 and GP4 tissues annotated from 50 unique digitized whole slide images (WSIs) of prostate needle core biopsy specimens stained with hematoxylin and eosin. The pyramidal digitized WSIs were extracted into image patches with a size of 256 x 256 pixels at a magnification of 20X. An ensemble approach is proposed combining U-Net-based architectures, including traditional U-Net, attention-based U-Net, and residual attention-based U-Net. This work initially considers a PCa tissue analysis using a combination of attention gate units with residual convolution units. The performance evaluation revealed a mean Intersection-over-Union of 0.79 for the two classes, 0.88 for the benign class, and 0.70 for the malignant class. The proposed method was then used to produce pixel-level segmentation maps of PCa adenocarcinoma tissue slides in the testing set. We developed a screening tool to discriminate between benign and malignant prostate tissue in digitized images of needle biopsy samples using an AI approach. We aimed to identify malignant adenocarcinoma tissues from our own collected, annotated, and organized dataset. Our approach returned the performance which was accepted by the pathologists.

基于注意力与残差U-Net集成架构的前列腺腺癌检测二元语义分割
准确判定格里森评分（Gleason Score）或格里森模式（Gleason Pattern, GP）在前列腺癌（prostate cancer, PCa）诊断中至关重要，因其是指导不同预后风险分组治疗决策的核心标准之一。然而，病理学家通过显微镜手动标注GP易出现误差，且观察者间差异显著。深度学习已被应用于数字化病理切片的GP自动识别，可辅助病理学家工作并降低观察者间差异，尤其在癌症早期GP识别中效果突出。
本研究针对前列腺腺癌的GP开展二元语义分割任务，通过分割区分良性与恶性组织，其中恶性类别涵盖从50例经苏木精-伊红（hematoxylin and eosin）染色的前列腺针芯活检标本数字化全玻片图像（whole slide images, WSIs）中标注的腺癌GP3与GP4组织。将金字塔式数字化全玻片图像按照20倍放大倍率裁剪为尺寸256×256像素的图像块。本文提出一种集成学习框架，融合多种基于U-Net的架构，包括传统U-Net、注意力U-Net（attention-based U-Net）以及残差注意力U-Net（residual attention-based U-Net）。本研究最初结合注意力门单元与残差卷积单元开展前列腺癌组织分析。
性能评估结果显示，两类样本的平均交并比（Intersection-over-Union, IoU）为0.79，良性类别交并比为0.88，恶性类别交并比为0.70。随后，所提方法被用于生成测试集内前列腺腺癌组织切片的像素级分割图。我们开发了一款基于人工智能方法的筛查工具，可在针芯活检样本的数字化图像中区分良性与恶性前列腺组织。本研究旨在从自主采集、标注并整理的数据集内识别恶性腺癌组织，所提方法的性能获得了病理学家的认可。