Towards Computation- and Communication-efficient Computational Pathology
Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application（广东省人工智能在医学影像分析与应用重点实验室）；Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences)（广东省人民医院（广东省医学科学院））；Southern Medical University（南方医科大学）
Despite the impressive performance across a wide range of applications, current computational pathology models face significant diagnostic efficiency challenges due to their reliance on high-magnification whole-slide image analysis. This limitation severely compromises their clinical utility, especially in time-sensitive diagnostic scenarios and situations requiring efficient data transfer. To address these issues, we present a novel computation- and communication-efficient framework called Magnification-Aligned Global-Local Transformer (MAG-GLTrans). Our approach significantly reduces computational time, file transfer requirements, and storage overhead by enabling effective analysis using low-magnification inputs rather than high-magnification ones. The key innovation lies in our proposed magnification alignment (MAG) mechanism, which employs self-supervised learning to bridge the information gap between low and high magnification levels by effectively aligning their feature representations. Through extensive evaluation across various fundamental CPath tasks, MAG-GLTrans demonstrates state-of-the-art classification performance while achieving remarkable efficiency gains: up to 10.7 times reduction in computational time and over 20 times reduction in file transfer and storage requirements. Furthermore, we highlight the versatility of our MAG framework through two significant extensions: (1) its applicability as a feature extractor to enhance the efficiency of any CPath architecture, and (2) its compatibility with existing foundation models and histopathology-specific encoders, enabling them to process low-magnification inputs with minimal information loss. These advancements position MAG-GLTrans as a particularly promising solution for time-sensitive applications, especially in the context of intraoperative frozen section diagnosis where both accuracy and efficiency are paramount.
IBM Research（IBM研究院）；EPFL（洛桑联邦理工学院）；ETH（苏黎世联邦理工学院）
Advances in entity-graph based analysis of histopathology images have brought in a new paradigm to describe tissue composition, and learn the tissue structure-to-function relationship. Entity-graphs offer flexible and scalable representations to characterize tissue organization, while allowing the incorporation of prior pathological knowledge to further support model interpretability and explainability. However, entity-graph analysis requires prerequisites for image-to-graph translation and knowledge of state-of-the-art machine learning algorithms applied to graph-structured data, which can po…
Comparative Analysis of Diffusion Generative Models in Computational Pathology
Concordia University（康考迪亚大学）；University of Montreal Hospital Center（蒙特利尔大学医院中心）；Kingston Health Sciences Center（金斯顿健康科学中心）；University of Calgary（卡尔加里大学）；Mila–Quebec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）
Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology
Institute of Pathology, Technical University of Munich（慕尼黑工业大学病理研究所）；Computer Aided Medical Procedures (CAMP), Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序（CAMP））；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Human Pathology, Juntendo University Graduate School of Medicine（顺天堂大学医学研究生院人体病理学部）；The Hong Kong University of Science and Technology（香港科技大学）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）
The field of computational pathology presents many challenges for computer vision algorithms due to the sheer size of pathology images. Histopathology images are large and need to be split up into image tiles or patches so modern convolutional neural networks (CNNs) can process them. In this work, we present a method to generate an interpretable image representation of computational pathology images using quadtrees and a pipeline to use these representations for highly accurate downstream classification. To the best of our knowledge, this is the first attempt to use quadtrees for pathology ima…
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…
University of Toronto（多伦多大学）；University of New Brunswick（新不伦瑞克大学）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；St. Michaels Hospital（圣迈克尔医院）；Huron Digital Pathology（休伦数字病理公司）；Kingston Health Sciences Center（金斯顿健康科学中心）
The lack of well-annotated datasets in computational pathology (CPath) obstructs the application of deep learning techniques for classifying medical images. %Since pathologist time is expensive, dataset curation is intrinsically difficult. Many CPath workflows involve transferring learned knowledge between various image domains through transfer learning. Currently, most transfer learning research follows a model-centric approach, tuning network parameters to improve transfer results over few datasets. In this paper, we take a data-centric approach to the transfer learning problem and examine t…
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Broad Institute of Harvard and MIT（哈佛-麻省理工学院博德研究所）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）
The rapidly emerging field of computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological features of clinical relevance. However, deep learning-based computational pathology approaches either require manual annotation of gigapixel whole slide images (WSIs) in fully-supervised settings or thousands of WSIs with slide-level labels in a weakly-supervised setting. Moreover, whole slide level computational pathology methods also suffer from domain adaptation and interpretability issues. These challenges have preve…
Radboud University Medical Center（拉德堡德大学医学中心）；Linköping University（林雪平大学）
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with images from one lab often underperform on unseen images from the other lab. Several techniques have been proposed to reduce the generalization error, mainly grouped into two categories: stain color augmentation and stain color normalization. The former simulates a wide variety of realistic stain variations during training, producing stain-invarian…
University of Toronto（多伦多大学）；Huron Digital Pathology Inc.（休伦数字病理公司）
One of the challenges facing the adoption of digital pathology workflows for clinical use is the need for automated quality control. As the scanners sometimes determine focus inaccurately, the resultant image blur deteriorates the scanned slide to the point of being unusable. Also, the scanned slide images tend to be extremely large when scanned at greater or equal 20X image resolution. Hence, for digital pathology to be clinically useful, it is necessary to use computational tools to quickly and accurately quantify the image focus quality and determine whether an image needs to be re-scanned.…
Unsupervised learning enables modeling complex images without the need for annotations. The representation learned by such models can facilitate any subsequent analysis of large image datasets. However, some generative factors that cause irrelevant variations in images can potentially get entangled in such a learned representation causing the risk of negatively affecting any subsequent use. The orientation of imaged objects, for instance, is often arbitrary/irrelevant, thus it can be desired to learn a representation in which the orientation information is disentangled from all other factors. …
Computational Pathology: A Survey Review and The Way Forward
Qualcomm AI Research（高通人工智能研究院）；Qualcomm Technologies Netherlands B.V.（高通科技荷兰有限公司）；Institute for Research in Immunology and Cancer of the University of Montreal（蒙特利尔大学免疫与癌症研究所）；University of Toronto（多伦多大学）
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…
From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology
Technical University Dresden（德累斯顿工业大学）；University of St Andrews（圣安德鲁斯大学）；Helmholtz Munich – German Research Center for Environment and Health（亥姆霍兹慕尼黑中心-德国环境与健康研究中心）；Technical University of Munich（慕尼黑工业大学）；University Hospital Aachen（亚琛大学医院）；University Medical Center Mainz（美因茨大学医学中心）；University of Leeds（利兹大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心 (NCT)）；University Hospital Heidelberg（海德堡大学医院）；Technische Universität Dresden（德累斯顿工业大学）
Benchmarking foundation models as feature extractors for weakly-supervised computational pathology
Technical University Dresden（德累斯顿工业大学）；StratifAI GmbH（StratifAI有限公司）；University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院附属医院）；National Center for Tumor Diseases Dresden (NCT/UCC)（德累斯顿国家肿瘤疾病中心（NCT/UCC））；German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心（NCT））；German Cancer Consortium (DKTK)（德国癌症联盟（DKTK））；Kepler University Hospital, Johannes Kepler University Linz（约翰内斯·开普勒大学林茨开普勒大学医院）；University of Bern（伯尔尼大学）；University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院）；University Medical Center Mainz（美因茨大学医学中心）；University Hospital Aachen（亚琛大学医院）；European Institute of Oncology IRCCS（欧洲肿瘤研究所IRCCS）；University Hospital Heidelberg（海德堡大学医院）
Sharing standardized image-derived data in computational pathology using DICOM
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（马萨诸塞州总医院阿西诺拉·A·马丁诺斯生物医学成像中心）；Harvard Medical School（哈佛医学院）；PixelMed Publishing（PixelMed出版公司）；Radical Imaging LLC（Radical成像有限责任公司）；Massachusetts General Hospital（马萨诸塞州总医院）；Stanford University（斯坦福大学）；University Hospital Erlangen（埃尔朗根大学医院）；Stony Brook University（石溪大学）；National Institute of Allergy and Infectious Diseases（美国国家过敏和传染病研究所）；Google Research（谷歌研究院）；Brigham and Women's Hospital（布莱根和妇女医院）
A Spatially-Aware Multiple Instance Learning Framework for Digital Pathology
Department of Biomedical Engineering, Eindhoven University of Technology（生物医学工程系，埃因霍温理工大学）；Mathematics and Computer Science Department, Eindhoven University of Technology（数学与计算机科学系，埃因霍温理工大学）
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…
IBM Research Zurich（苏黎世IBM研究公司）；EPFL Lausanne（洛桑联邦理工学院）；ETH Zurich（苏黎世联邦理工学院）；Fondazione Pascale（帕斯卡尔基金会）；University of Bern（伯尔尼大学）；Uppsala University（乌普萨拉大学）
Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists. In this work, we address this by adopting biological entity-based graph processing and graph explainers enabling explanations accessible to pathologists. In this context, a major challenge becomes to discern meaningful explainers, particularly in a standardized and quantifiable fashio…
Normalizing unwanted color variations due to differences in staining processes and scanner responses has been shown to aid machine learning in computational pathology. Of the several popular techniques for color normalization, structure preserving color normalization (SPCN) is well-motivated, convincingly tested, and published with its code base. However, SPCN makes occasional errors in color basis estimation leading to artifacts such as swapping the color basis vectors between stains or giving a colored tinge to the background with no tissue. We made several algorithmic improvements to remove…
MOOZY: A Patient-First Foundation Model for Computational Pathology
CSSE, Concordia University, Montr\' e al, Canada Mila -- Qu\' e bec AI Institute, Montr\' e al, Canada CHUM, Universit\' e de Montr\' e al, Montr\' e al, Canada IRIC, Universit\' e de Montr\' e al, Montr\' e al, Canada Universit\' e de Montr\' e al, Montr\' e al, Canada Canada CIFAR Chair, Polytechnique Montr\' e al, Montr\' e al, Canada Dept.\ of Pathology, McGill University, Montr\' e al, Canada
Virchow: A Million-Slide Digital Pathology Foundation Model
Paige；Microsoft Research（微软研究院）；NSW Health Pathology（新南威尔士州卫生病理学）；St George Hospital（圣乔治医院）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；University of Rochester（罗切斯特大学）
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs
University of Stavanger（斯塔万格大学）；University Medical Center Rotterdam（鹿特丹大学医学中心）；Erasmus MC Cancer Institute（伊拉斯姆斯大学医学中心癌症研究所）；Stavanger University Hospital（斯塔万格大学医院）；INCLIVA Biomedical Research Institute（INCLIVA生物医学研究所）；University of Valencia（瓦伦西亚大学）
HistoEncoder: a digital pathology foundation model for prostate cancer
University of Helsinki（赫尔辛基大学）；Helsinki University Hospital（赫尔辛基大学医院）；Skåne University Hospital（斯科讷大学医院）；Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki（赫尔辛基大学HiLIFE项目下属芬兰分子医学研究所（FIMM））；iCAN Digital Precision Cancer Medicine Flagship（iCAN数字精准癌症医学旗舰项目）
Integrating Pathology Foundation Models and Spatial Transcriptomics for Cellular Decomposition from Histology Images
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA（电气与计算机工程学院，佐治亚理工学院）；Department of Biomedical Engineering, Georgia Institute of Technology（生物医学工程系，佐治亚理工学院）；Emory University, Atlanta, GA, USA（埃默里大学）
Generative Deep Learning for Computational Destaining and Restaining of Unregistered Digital Pathology Images
Department of Computer Science University of California Irvine, CA, USA（计算机科学系 加州大学 伊藤市 加州 USA）；Department of Pathology Stanford University Stanford, CA, USA（病理学系 斯坦福大学 斯坦福市 加州 USA）
GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
Microsoft Research（微软研究院）；Paul G. Allen School of Computer Science and Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Providence Genomics（普罗维登斯基因组学公司）；Earle A. Chiles Research Institute, Providence Cancer Institute（普罗维登斯癌症研究所厄尔·A·奇尔斯研究所）；Providence Research Network（普罗维登斯研究网络）
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 t…
TU Darmstadt（达姆施塔特工业大学）；University Hospital Cologne（科隆大学医院）
Although deep federated learning has received much attention in recent years, progress has been made mainly in the context of natural images and barely for computational pathology. However, deep federated learning is an opportunity to create datasets that reflect the data diversity of many laboratories. Further, the effort of dataset construction can be divided among many. Unfortunately, existing algorithms cannot be easily applied to computational pathology since previous work presupposes that data distributions of laboratories must be similar. This is an unlikely assumption, mainly since dif…
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）；Technische Universität Berlin（柏林工业大学）
Image analysis tasks in computational pathology are commonly solved using convolutional neural networks (CNNs). The selection of a suitable CNN architecture and hyperparameters is usually done through exploratory iterative optimization, which is computationally expensive and requires substantial manual work. The goal of this article is to evaluate how generic tools for neural network architecture search and hyperparameter optimization perform for common use cases in computational pathology. For this purpose, we evaluated one on-premises and one cloud-based tool for three different classificati…
CAMP: Continuous and Adaptive Learning Model in Pathology
School of Electrical Engineering, Korea University（高丽大学电气工程学院）；Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine（成均馆大学医学院江北三星医院）；Pathology Center, Seegene Medical Foundation（Seegene医学基金会病理中心）
Large amounts of unlabelled data are commonplace for many applications in computational pathology, whereas labelled data is often expensive, both in time and cost, to acquire. We investigate the performance of unsupervised and supervised deep learning methods when few labelled data are available. Three methods are compared: clustering autoencoder latent vectors (unsupervised), a single layer classifier combined with a pre-trained autoencoder (semi-supervised), and a supervised CNN. We apply these methods on hematoxylin and eosin (H&E) stained prostatectomy images to classify tumour versus non-…
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…
Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem. Here, we propose STRAP (Style TRansfer Augmentation for histoPathology), a form of data augmentation based on random style transfer from non-medical style source such as artistic paintings, for learni…
In this work we show preliminary results of deep multi-task learning in the area of computational pathology. We combine 11 tasks ranging from patch-wise oral cancer classification, one of the most prevalent cancers in the developing world, to multi-tissue nuclei instance segmentation and classification.
Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, China（新型一代人工智能技术及其交叉应用重点实验室（东南大学），教育部，南京，中国）；School of Biomedical Engineering, Tsinghua University（生物医学工程学院，清华大学）；Department of Biomedical Engineering and Department of Electrical and Computer Engineering, National University of Singapore（生物医学工程系和电子与计算机工程系，新加坡国立大学）；School of Information Science and Engineering, Southeast University（信息科学与工程学院，东南大学）；Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University（流行病学系，全球健康中心，公共卫生学院，南京医科大学）；Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore（高性能计算研究所，科技研究局，新加坡）
Artificial Intelligence for Digital and Computational Pathology
Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Broad Institute of Harvard and MIT（哈佛大学与麻省理工学院博德研究所）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Massachusetts Institute of Technology（麻省理工学院）
University of Warwick（华威大学）；University of Nottingham（诺丁汉大学）；Menoufia University（曼努菲亚大学）；University Hospital Coventry and Warwickshire（考文垂和华威大学医院）；Gunma University（群马大学）；University Hospitals Leicester（莱斯特大学医院）
Recent advances in whole slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence (AI) based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilize information embedded in pathology WSIs beyond what we obtain through visual assessment. For automated analysis of WSIs and validation of machine learning (ML) models, annotations at the slide, tissue and cellular levels are required. The annotation of important visual constructs in pathology images is an important compon…
East China Normal University（华东师范大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Shanghai Jiaotong University（上海交通大学）
Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety of applications relied on high-precision pathology image segmentation, such as computational pathology and precision medicine. Since hyperspectral pathology images benefit from the rich and detailed spectral information even beyond the visible spectrum, the key to achieve high-precision hyperspectral pathology image segmentation is to felicitously model the context along high-dimensional spectral bands. Inspired by the strong context modeling ability of transformers, we hereby, for the first time, formulate the contextual…
