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值语义分割

准确判定格里森评分（Gleason Score）或格里森模式（Gleason Pattern, GP）是前列腺癌（Prostate Cancer, PCa）诊断中的关键环节，因其是指导预后风险分层患者治疗方案选择的核心标准之一。然而，病理学家通过显微镜人工标注格里森模式的过程易出现误差，且观察者间差异显著。深度学习技术已被应用于数字化病理切片中格里森模式的自动区分，可辅助病理学家工作并降低观察者间差异，在癌症早期格里森模式识别中效果尤佳。

本研究针对前列腺腺癌的格里森模式开展二值语义分割研究。该分割任务将组织区分为良性与恶性两类，其中恶性类别包含腺癌GP3与GP4组织，标注数据源自50例经苏木精-伊红（hematoxylin and eosin, HE）染色的前列腺针芯活检标本数字化全切片图像（Whole Slide Images, WSIs）。金字塔格式的数字化全切片图像被裁剪为20倍放大倍率下、尺寸为256×256像素的图像块。

本研究提出一种集成学习方案，融合三类基于U-Net的架构：传统U-Net、注意力U-Net以及残差注意力U-Net。本研究初始采用注意力门单元与残差卷积单元结合的方案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，其中良性类别IoU为0.88，恶性类别IoU为0.70。

随后将所提方法应用于测试集，生成前列腺腺癌组织切片的像素级分割图。本研究基于人工智能方法开发了一款筛查工具，可对针芯活检标本的数字化图像中的前列腺良恶性组织进行区分。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。本研究所提方法的性能获得了病理学家的认可。

搜集汇总

背景与挑战

背景概述

该数据集专注于前列腺癌的二进制语义分割，用于检测前列腺腺癌，包含50个独特的数字化全切片图像（WSIs），图像被提取为256x256像素的补丁。研究采用基于U-Net的集成方法，包括注意力机制和残差结构，性能评估显示平均IoU为0.79，旨在辅助病理学家减少诊断误差。数据集包括训练好的模型文件和原始幻灯片数据，总大小为15.56 GB。