Machine-Learning-Based Diagnostics of EEG Pathology
Medical Center – University of Freiburg（弗莱堡大学医学中心）；University of Freiburg（弗莱堡大学）；Machine Learning Lab（机器学习实验室）；Neuromedical AI Lab（神经医学人工智能实验室）；Neurorobotics Lab（神经机器人学实验室）
Lukas Alexander Wilhelm Gemein, Robin Tibor Schirrmeister, Patryk Chrabąszcz, Daniel Wilson, Joschka Boedecker, Andreas Schulze-Bonhage, Frank Hutter, Tonio Ball
Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding have typically analyzed a limited number of features, decoders, or both. For a I) more elaborate feature-based EEG analysis, and II) in-depth comparisons of both approaches, here we first develop a comprehensive feature-based framework, and then compare this framework to state-of-the-art end-to-end methods. To this aim, we apply the proposed feature-based framework and deep neural networks including an EEG-optimized temporal convolutional network (TCN) to the task of pathological versus non-pathological EEG classification. For a robust comparison, we chose the Temple University Hospital (TUH) Abnormal EEG Corpus (v2.0.0), which contains approximately 3000 EEG recordings. The results demonstrate that the proposed feature-based decoding framework can achieve accuracies on the same level as state-of-the-art deep neural networks. We find accuracies across both approaches in an astonishingly narrow range from 81--86\%. Moreover, visualizations and analyses indicated that both approaches used similar aspects of the data, e.g., delta and theta band power at temporal electrode locations. We argue that the accuracies of current binary EEG pathology decoders could saturate near 90\% due to the imperfect inter-rater agreement of the clinical labels, and that such decoders are already clinically useful, such as in areas where clinical EEG experts are rare. We make the proposed feature-based framework available open source and thus offer a new tool for EEG machine learning research.
The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status and scoring of several established biomarkers, including ER, PGR, HER2 and KI67. However, this is a task that can also be facilitated by computational pathology image analysis methods. The research in computational pathology has recently made numerous substantial advances, often …
Linköping University（林雪平大学）；Center for Medical Image Science and Visualization, Linköping University（林雪平大学医学影像科学与可视化中心）；Sectra AB
Artificial intelligence (AI) has shown great promise for diagnostic imaging assessments. However, the application of AI to support medical diagnostics in clinical routine comes with many challenges. The algorithms should have high prediction accuracy but also be transparent, understandable and reliable. Thus, explainable artificial intelligence (XAI) is highly relevant for this domain. We present a survey on XAI within digital pathology, a medical imaging sub-discipline with particular characteristics and needs. The review includes several contributions. Firstly, we give a thorough overview of…
Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy
University of Leeds（利兹大学）；Leeds Teaching Hospitals NHS Trust（利兹教学医院NHS信托）；Linköping University（林雪平大学）；Centre for Medical Image Science and Visualization (CMIV), Linköping University（林雪平大学医学图像科学与可视化中心）
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…
University of California San Diego（加州大学圣迭戈分校）；Mayo Clinic（妙佑医疗国际）
An optical microscopic examination of thinly cut stained tissue on glass slides prepared from a FFPE tissue blocks is the gold standard for tissue diagnostics. In addition, the diagnostic abilities and expertise of any pathologist is dependent on their direct experience with common as well as rarer variant morphologies. Recently, deep learning approaches have been used to successfully show a high level of accuracy for such tasks. However, obtaining expert-level annotated images is an expensive and time-consuming task and artificially synthesized histological images can prove greatly beneficial…
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics
University of Concordia（康考迪亚大学）；University of Montreal（蒙特利尔大学）；Sunnybrook Health Science Centre（桑尼布鲁克健康科学中心）；Kingston General Hospital（金斯顿总医院）；Mila–Quebec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）
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…
DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Pathology, Liuzhou People’s Hospital Affiliated to Guangxi Medical University（广西医科大学柳州市人民医院病理科）；Department of Immunology, College of Basic Medical Sciences, China Medical University（中国医科大学基础医学学院免疫科）；Greater Bay Area Center for Medical Device Evaluation and Inspection.NMPA（粤港澳大湾区医疗器械评价和检验中心.NMPA）；Shenzhen Shengqiang Technology Co., Ltd.（深圳盛强科技有限公司）；Department of Pathology, Chongqing University Affiliated Three Gorges Hospital（重庆大学附属第三人民医院病理科）
Proscia Inc.（Proscia 公司）；Boonshoft School of Medicine, Wright State University School of Medicine（莱特州立大学医学院布恩肖夫特医学院）；Dermatopathology Laboratory of Central States（中部各州皮肤病理学实验室）；University of Florida College of Medicine（佛罗里达大学医学院）；Cockerell Dermatopathology（科克雷尔皮肤病理学机构）；Sidney Kimmel Medical College at Thomas Jefferson University（托马斯杰斐逊大学西德尼·金梅尔医学院）
Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin \& eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors. The system's …
University of Warwick（华威大学）；University of Birmingham（伯明翰大学）；University Hospitals Coventry & Warwickshire（考文垂和沃里克郡大学医院）；The Alan Turing Institute（艾伦·图灵研究所）
Computational Pathology (CPath) is an emerging field concerned with the study of tissue pathology via computational algorithms for the processing and analysis of digitized high-resolution images of tissue slides. Recent deep learning based developments in CPath have successfully leveraged sheer volume of raw pixel data in histology images for predicting target parameters in the domains of diagnostics, prognostics, treatment sensitivity and patient stratification -- heralding the promise of a new data-driven AI era for both histopathology and oncology. With data serving as the fuel and AI as th…
Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT)（先进磁共振诊断与治疗技术国家工程研究中心）；Med-X Research Institute, Shanghai Jiao Tong University（Med-X研究院，上海交通大学）；Department of Spine Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院脊柱外科部）；Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院放射科）；Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院放射科）
Deep learning (DL) has shown great potential in digital pathology applications. The robustness of a diagnostic DL-based solution is essential for safe clinical deployment. In this work we evaluate if adding uncertainty estimates for DL predictions in digital pathology could result in increased value for the clinical applications, by boosting the general predictive performance or by detecting mispredictions. We compare the effectiveness of model-integrated methods (MC dropout and Deep ensembles) with a model-agnostic approach (Test time augmentation, TTA). Moreover, four uncertainty metrics are…
A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation
Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China（南方医科大学南芳医院病理科，广州，中国）；Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China（南方医科大学基础医学学院病理科，广州，中国）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China（香港科技大学计算机科学与工程系，香港，中国）；Guangdong Provincial Key Laboratory of Molecular Tumor Pathology, Guangzhou, China（广东省分子肿瘤病理重点实验室，广州，中国）；Department of Pathology, Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China（山东省青岛坊山医院病理科，济南，山东，中国）
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）
From Pixels to Pathology: Restoration Diffusion for Diagnostic-Consistent Virtual IHC
organization= Institute of Pathology, Technical University of Munich, TUM School of Medicine；organization= Computer Aided Medical Procedures (CAMP), TU Munich , city= Munich , country= Germany；organization= Munich Center for Machine Learning (MCML) , city= Munich , country= Germany；organization= TUM School of Computation, Information；organization= Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences , city= Mashhad , country= Iran；organization= Munich Data Science Institute (MDSI) , city= Munich , country= Germany
Mars Digital Technologies（玛氏数字科技）；Antech Diagnostics（Antech诊断公司）；Stanford University（斯坦福大学）
Background: Histopathology is an important modality for the diagnosis and management of many diseases in modern healthcare, and plays a critical role in cancer care. Pathology samples can be large and require multi-site sampling, leading to upwards of 20 slides for a single tumor, and the human-expert tasks of site selection and and quantitative assessment of mitotic figures are time consuming and subjective. Automating these tasks in the setting of a digital pathology service presents significant opportunities to improve workflow efficiency and augment human experts in practice. Approach: Mul…
Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）
Various screening and diagnostic methods have led to a large reduction of cervical cancer death rates in developed countries. However, cervical cancer is the leading cause of cancer related deaths in women in India and other low and middle income countries (LMICs) especially among the urban poor and slum dwellers. Several sophisticated techniques such as cytology tests, HPV tests etc. have been widely used for screening of cervical cancer. These tests are inherently time consuming. In this paper, we propose a convolutional autoencoder based framework, having an architecture similar to SegNet w…
