In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images. The descriptor is based on the Radon transform wherein we apply parallel projections in small local neighborhoods of gray-level images. Using equidistant projection directions in each window, we extract unique and invariant characteristics of the neighborhood by taking the intersection of adjacent projections. Thereafter, we construct a histogram for each image, which we call the FLIP histogram. Various resolutions provide different FLIP histograms which are then concatenated to form the mFLIP descriptor. Our experiments included training common networks from scratch and fine-tuning pre-trained networks to benchmark our proposed descriptor. Experiments are conducted on the publicly available dataset KIMIA Path24 and KIMIA Path960. For both of these datasets, FLIP and mFLIP descriptors show promising results in all experiments.Using KIMIA Path24 data, FLIP outperformed non-fine-tuned Inception-v3 and fine-tuned VGG16 and mFLIP outperformed fine-tuned Inception-v3 in feature extracting.
LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images
College of Computer Science and Electronic Engineering（计算机科学与电子工程学院）；Third Xiangya Hospital（湘雅三医院）；Hunan University（湖南大学）；Central South University（中南大学）
Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence
Huron Digital Pathology（休伦数字病理学公司）；University of Waterloo（滑铁卢大学）；Vector Institute（矢量研究所）；McMaster University（麦克马斯特大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）
Importance of Preprocessing in Histopathology Image Classification Using Deep Convolutional Neural Network
Burdur Mehmet Akif Ersoy University（布尔杜尔穆罕默德·阿基夫·埃尔索伊大学）；Süleyman Demirel University（苏莱曼·德米雷尔大学）
本研究的目的是提出一种替代和混合解决方案的方法来诊断疾病的组织病理学图像取自动物副结核和完整的肠道。详细地说，混合方法是基于使用图像处理和深度学习来获得更好的结果。从组织病理学图像进行可靠的疾病检测是医学图像处理中的一个公开问题，需要开发替代解决方案。在这种情况下，在与Burdur Mehmet Akif Ersoy大学兽医学院和病理学系的联合研究中收集了520张组织病理学图像。手动检测和解释这些图像需要专业知识和大量的处理时间。因此，兽医，特别是新招募的医生，在开发这种疾病的检测和治疗方法时，非常需要成像和计算机视觉系统。本研究提出的解决方法是将CLAHE方法与图像处理结合使用。在此预处理之后，通过对由VGG-16架构支持的卷积神经网络进行分类来进行诊断。该方法使用完全原始的数据集图像。两种类型的系统应用于评价参数。而F1分数是93%的方法分类没有数据预处理，它是98%的方法，用CLAHE方法预处理。
SWAN -- Enabling Fast and Mobile Histopathology Image Annotation through Swipeable Interfaces
Flensburg University of Applied Sciences（弗劳恩霍夫应用科技大学）；University of Veterinary Medicine（兽医大学）；Schwarzman Animal Medical Center（施瓦茨曼动物医疗中心）；Medical University of Vienna（维也纳医学大学）；Technische Hochschule Ingolstadt（英戈尔施塔特技术大学）
Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision Mamba
School of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程学院）；School of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程学院）；School of Electrical Engineering, Shahrood Branch, Islamic Azad University（伊斯兰阿扎德大学沙赫鲁德分校电气工程学院）；Medical Sciences and Technologies Department, Science and Research Branch, Islamic Azad University（伊斯兰阿扎德大学科学研究分支医学科学与技术部门）
A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection
Austrian Center for Medical Innovation and Technology（奥地利医学创新与技术中心）；Danube Private University（多瑙河私立大学）；Medical University of Vienna（维也纳医科大学）；Patho im Zentrum（中心病理机构）；Institute for Systems and Robotics (ISR-Lisboa/LARSyS)（系统与机器人研究所（里斯本系统与机器人研究所/卢西塔尼亚机器人与系统协会））；Instituto Superior Técnico（高等技术学院）；University of Lisbon（里斯本大学）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer
Northeastern University（东北大学）；Cancer Hospital, China Medical University（中国医科大学肿瘤医院）；Liaoning Cancer Hospital and Institute（辽宁省肿瘤医院暨研究所）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；University of Luebeck（吕贝克大学）
Histopathology WSI Encoding based on GCNs for Scalable and Efficient Retrieval of Diagnostically Relevant Regions
Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University（北京航空航天大学生物医学工程北京高级创新中心）；School of Software, Hefei University of Technology（合肥工业大学软件学院）；Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences（中国科学院天津工业生物技术研究所）
AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives
Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London（医学图像计算中心，医学物理与生物医学工程系，伦敦大学学院）；Div of Surgery & Interventional Sci, University College London（外科与介入科学部，伦敦大学学院）；School of Computer Science and Engineering, University of Westminster（计算机科学与工程学院，威斯敏斯特大学）
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis
Institute of Medical Science and Technology, Shahid Beheshti University（沙希德·贝赫什提大学医学科学与技术研究所）
Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping
Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)（生物医学计算成像与模拟技术中心）；School of Computing, University of Leeds（利兹大学计算机学院）；Leeds Institute of Medical Research at St James’s, School of Medicine, University of Leeds（利兹大学医学院圣詹姆斯利兹医学研究所）；Leeds Cancer Centre, St James’s University Hospital（圣詹姆斯大学医院利兹癌症中心）
