Computational pathology is a field that has complemented various subspecialties of diagnostic pathology over the last few years. In this article a brief analyzis the different applications in nephrology is developed. To begin, an overview of the different forms of image production is provided. To continue, the most frequent applications of computer vision models, the salient features of the different clinical applications, and the data protection considerations encountered are described. To finish the development, I delve into the interpretability of these applications, expanding in depth on the three dimensions of this area.
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Computer-assisted quantitative analysis on Giga-pixel pathology images has provided a new avenue in histology examination. The innovations have been largely focused on cancer pathology (i.e., tumor segmentation and characterization). In non-cancer pathology, the learning algorithms can be asked to examine more comprehensive tissue types simultaneously, as a multi-label setting. The prior arts typically needed to train multiple segmentation networks in order to match the domain-specific knowledge for heterogeneous tissue types (e.g., glomerular tuft, glomerular unit, proximal tubular, distal tu…
Renal digital pathology visual knowledge search platform based on language large model and book knowledge
College of Information Technology, ZheJiang Shuren University（浙江树人大学信息技术学院）；The First Affiliated Hospital, School of Medicine, Zhejiang University（浙江大学医学院附属第一医院）；Chinese Academy of Medical Science（中国医学科学院）；Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare（阿里巴巴-浙江大学未来数字医疗联合研究中心）；Zhejiang University School of Medicine（浙江大学医学院）
Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Indolent RCC is often low-grade without necrosis and can be monitored without treatment. Aggressive RCC is often high-grade and can cause metastasis and death if not promptly detected and treated. While most kidney cancers are detected on CT scans, grading is based on histology from invasive biopsy or surgery. Determining aggressiveness on CT images is clinically important as it facilitates risk stratification and treatment planning. This study aims to use machine learning methods to identify radiology features tha…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Considerable morphological phenotyping studies in nephrology have emerged in the past few years, aiming to discover hidden regularities between clinical and imaging phenotypes. Such studies have been largely enabled by deep learning based image analysis to extract sparsely located targeting objects (e.g., glomeruli) on high-resolution whole slide images (WSI). However, such methods need to be trained using labor-intensive high-quality annotations, ideally labeled by pathologists. Inspired by the recent "human-in-the-loop" strategy, we developed EasierPath, an open-source tool to integrate huma…
An Accelerated Pipeline for Multi-label Renal Pathology Image Segmentation at the Whole Slide Image Level
Vanderbilt University（范德堡大学）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Vanderbilt University Medical Center（范德堡大学医学中心）；Georgia State University（佐治亚州立大学）
Knowledge-enhanced Pretraining for Vision-language Pathology Foundation Model on Cancer Diagnosis
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属新华医院）；School of Artificial Intelligence（人工智能学院）；Department of Pathology（病理学部）；Department of Oral Pathology（口腔病理学部）；Department of Pediatric Hematology/Oncology（儿科血液肿瘤科）；Clinical Research and Innovation Unit（临床研究与创新单元）；Department of Pediatric Cardiology（儿童心脏病科）
Recent works show that Generative Adversarial Networks (GANs) can be successfully applied to chest X-ray data augmentation for lung disease recognition. However, the implausible and distorted pathology features generated from the less than perfect generator may lead to wrong clinical decisions. Why not keep the original pathology region? We proposed a novel approach that allows our generative model to generate high quality plausible images that contain undistorted pathology areas. The main idea is to design a training scheme based on an image-to-image translation network to introduce variation…
New York University（纽约大学）；IIIT Hyderabad（印度海得拉巴国际信息技术研究所）
Systemic lupus erythematosus (SLE) is an autoimmune disease in which the immune system of the patient starts attacking healthy tissues of the body. Lupus Nephritis (LN) refers to the inflammation of kidney tissues resulting in renal failure due to these attacks. The International Society of Nephrology/Renal Pathology Society (ISN/RPS) has released a classification system based on various patterns observed during renal injury in SLE. Traditional methods require meticulous pathological assessment of the renal biopsy and are time-consuming. Recently, computational techniques have helped to allevi…
Charles Sturt University（查尔斯特大学）；University of Technology Sydney（悉尼科技大学）；Wollongong Hospital（伍伦贡医院）
The application of computer vision for COVID-19 diagnosis is complex and challenging, given the risks associated with patient misclassifications. Arguably, the primary value of medical imaging for COVID-19 lies rather on patient prognosis. Radiological images can guide physicians assessing the severity of the disease, and a series of images from the same patient at different stages can help to gauge disease progression. Hence, a simple method based on lung-pathology interpretable features for scoring disease severity from Chest X-rays is proposed here. As the primary contribution, this method …
University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
Pseudo-healthy synthesis is the task of creating a subject-specific `healthy' image from a pathological one. Such images can be helpful in tasks such as anomaly detection and understanding changes induced by pathology and disease. In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy. We disentangle what appears to be healthy and where disease is as a segmentation map, which are then recombined by a network to reconstruct the input disease image. We train our models adversarially using either paired or unpaired settings, …
A Disease-Centric Vision-Language Foundation Model for Precision Oncology in Kidney Cancer
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China（复旦大学基础医学学院数字医学研究中心）；Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China（上海市医疗影像计算与计算机辅助干预重点实验室）；Department of Urology, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, China（山东大学齐鲁医院泌尿科）；Microsoft Research Asia, Shanghai, 200232, China（微软亚洲研究院）；Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China（浙江大学医学院附属第一医院放射科）；Center of Health data science, Linyi People’s Hospital, Shandong, 276003, China（临沂人民医院健康数据科学中心）；Shandong Open Laboratory of Data Innovation Application, Shandong, 276003, China（山东省数据创新应用开放实验室）；Department of Radiology, the First People’s Hospital of Lianyungang, Lianyungang, 222002, China（连云港第一人民医院放射科）；Department of Urology, Zhangye People’s Hospital affiliated to Hexi University, Zhangye, 734000, China（张掖人民医院（河西大学附属）泌尿科）；Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China（上海交通大学附属瑞金医院泌尿科）；Department of Urology, Linyi People’s Hospital, Shandong, 276003, China（临沂人民医院泌尿科）；Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China（复旦大学中山医院泌尿科）
Apple orchards in the U.S. are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs, environmental, and health impacts. We have manually captured 3,651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset, expert-annotated to create a pilot dataset for apple scab, cedar apple ru…
Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎大脑研究所（ICM））；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共援助医院）；Hôpital Pitié Salpêtrière（皮蒂耶-萨尔佩特里埃医院）
Non-invasive brain imaging techniques allow understanding the behavior and macro changes in the brain to determine the progress of a disease. However, computational pathology provides a deeper understanding of brain disorders at cellular level, able to consolidate a diagnosis and make the bridge between the medical image and the omics analysis. In traditional histopathology, histology slides are visually inspected, under the microscope, by trained pathologists. This process is time-consuming and labor-intensive; therefore, the emergence of Computational Pathology has triggered great hope to ea…
ESIEE-Paris, University Paris-Est Marne-la-Vallée（巴黎东马恩拉瓦莱大学ESIEE巴黎）；Faculty of medicine, University of Monastir（莫纳斯提尔大学医学院）；ISITCom Hammam-Sousse, University of Sousse（苏斯大学哈马姆苏斯高等信息与通信技术学院）
Ocular pathology detection from fundus images presents an important challenge on health care. In fact, each pathology has different severity stages that may be deduced by verifying the existence of specific lesions. Each lesion is characterized by morphological features. Moreover, several lesions of different pathologies have similar features. We note that patient may be affected simultaneously by several pathologies. Consequently, the ocular pathology detection presents a multi-class classification with a complex resolution principle. Several detection methods of ocular pathologies from fundu…
Johns Hopkins University（约翰斯·霍普金斯大学）；Sigma Technologies（12西格玛科技）；Johns Hopkins School of Medicine（约翰斯·霍普金斯医学院）
Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely. To address this challenge, we propose a Bayesian deep learning method that learns to translate healthy computed tomography images to magnetic resonance images and simultaneously calculates voxel-wise uncertainty. Since high uncertainty occurs in pathological regions of the image, this uncertainty can be used for unsupervised anomaly segmentation. We show enc…
SUNY Buffalo（纽约州立大学布法罗分校）；Medical College of Wisconsin（威斯康星医学院）；Washington University School of Medicine（华盛顿大学医学院）；University of California, Davis Medical Center（加州大学戴维斯医学中心）
Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the commonly used whole slide image (WSI) viewer, Aperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and display of neural network predictions on WSIs. Leveraging this, we propose the use of a human-in-the-loop strategy to reduce the burden of WSI annotation. We …
Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Zhongnan Hospital of Wuhan University（武汉大学中南医院）；Fuzhou University Affiliated Provincial Hospital（福州大学附属省立医院）；Liuzhou People's Hospital Affiliated to Guangxi Medical University（广西医科大学附属柳州市人民医院）；The Second Affiliated Hospital of Southern University of Science and Technology（南方科技大学第二附属医院）；Shenzhen Zhengjingda Instrument Co., Ltd.（深圳正景达仪器有限公司）；Chongqing University Affiliated Three Gorges Hospital（重庆大学附属三峡医院）；Shenzhen Shengqiang Technology Co., Ltd.（深圳盛强科技有限公司）；Longgang Central Hospital of Shenzhen（深圳市龙岗中心医院）；Ningbo Clinical Pathology Diagnosis Center（宁波市临床病理诊断中心）
Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets
Universidad Politécnica de Madrid（马德里理工大学）；Centro Nacional de Investigaciones Cardiovasculares (CNIC)（国家心血管研究中心）；Universidad Complutense de Madrid（马德里康普顿斯大学）；Center for Computational Simulation (CCS)（计算仿真中心）
Histopathology images; microscopy images of stained tissue biopsies contain fundamental prognostic information that forms the foundation of pathological analysis and diagnostic medicine. However, diagnostics from histopathology images generally rely on a visual cognitive assessment of tissue slides which implies an inherent element of interpretation and hence subjectivity. Access to digitized histopathology images enabled the development of computational systems aiming at reducing manual intervention and automating parts of pathologists' workflow. Specifically, applications of deep learning to…
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）
The emergence of digital pathology has opened new horizons for histopathology and cytology. Artificial-intelligence algorithms are able to operate on digitized slides to assist pathologists with diagnostic tasks. Whereas machine learning involving classification and segmentation methods have obvious benefits for image analysis in pathology, image search represents a fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologist a novel approach to improve diagnostic accuracy through visual inspection of similar c…
A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images
Hunan University（湖南大学）；Central South University（中南大学）；Third Xiangya Hospital（湘雅三医院）
Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment plans, treatment cycles, and cure rates, and helps doctors open up innovative approaches to patient prognosis. In the past decade, rapid advances in artificial intelligence, chip design and manufacturing, and mobile computing have facilitated research in computational pathology and have the potential to provide better-integrated solutions for whole-slide im…
