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 classification tasks for histological images: tissue classification, mutation prediction, and grading.
We found that the default CNN architectures and parameterizations of the evaluated AutoML tools already yielded classification performance on par with the original publications. Hyperparameter optimization for these tasks did not substantially improve performance, despite the additional computational effort. However, performance varied substantially between classifiers obtained from individual AutoML runs due to non-deterministic effects.
Generic CNN architectures and AutoML tools could thus be a viable alternative to manually optimizing CNN architectures and parametrizations. This would allow developers of software solutions for computational pathology to focus efforts on harder-to-automate tasks such as data curation.
Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet（卡罗林斯卡研究所医学流行病学与生物统计学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）
Recommendations on test datasets for evaluating AI solutions in pathology
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；Technische Universität Berlin（柏林工业大学）；Carl Gustav Carus University Hospital Dresden (UKD)（德累斯顿卡尔·古斯塔夫·卡鲁斯大学医院（UKD））；TU Dresden (TUD)（德累斯顿工业大学（TUD））；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt Universität zu Berlin（柏林洪堡大学）；University Hospital RWTH Aachen（亚琛工业大学医院）；Medical University of G（G医科大学）
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）
PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis
Business AI Lab, College of Technology, National Economics University（商业人工智能实验室，技术学院，越南国民经济大学）；A2I Lab, Phenikaa School of Computing, Phenikaa University（A2I实验室，Ph Phenikaa大学计算学院）；Radiology and Functional Exploration Center, Phenikaa University Hospital（放射学与功能探索中心，Phenikaa大学医院）
Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases
IBM Research Africa（IBM非洲研究院）；National Cancer Registry（国家癌症登记处）
Like most global cancer registries, the National Cancer Registry in South Africa employs expert human coders to label pathology reports using appropriate International Classification of Disease for Oncology (ICD-O) codes spanning 42 different cancer types. The annotation is extensive for the large volume of cancer pathology reports the registry receives annually from public and private sector institutions. This manual process, coupled with other challenges results in a significant 4-year lag in reporting of annual cancer statistics in South Africa. We present a hierarchical deep learning ensem…
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-…
DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Images
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）
Southeast University（东南大学）；Shenzhen University（深圳大学）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Kings College London（伦敦国王学院）；Affiliated Drum Tower Hospital of Nanjing University Medical School（南京大学医学院附属鼓楼医院）
Multi-sequence cardiac magnetic resonance (CMR) provides essential pathology information (scar and edema) to diagnose myocardial infarction. However, automatic pathology segmentation can be challenging due to the difficulty of effectively exploring the underlying information from the multi-sequence CMR data. This paper aims to tackle the scar and edema segmentation from multi-sequence CMR with a novel auto-weighted supervision framework, where the interactions among different supervised layers are explored under a task-specific objective using reinforcement learning. Furthermore, we design a c…
Sejong University（世宗大学）；Kangbuk Samsung Hospital（江北三星医院）；Sungkyunkwan University School of Medicine（成均馆大学医学院）；Korea University（高丽大学）
An automated segmentation and classification of nuclei is an essential task in digital pathology. The current deep learning-based approaches require a vast amount of annotated datasets by pathologists. However, the existing datasets are imbalanced among different types of nuclei in general, leading to a substantial performance degradation. In this paper, we propose a simple but effective data augmentation technique, termed GradMix, that is specifically designed for nuclei segmentation and classification. GradMix takes a pair of a major-class nucleus and a rare-class nucleus, creates a customiz…
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）
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 British Columbia（不列颠哥伦比亚大学）；Vancouver General Hospital（温哥华综合医院）；BC Cancer Research Center（不列颠哥伦比亚癌症研究中心）
Ovarian cancer is the most lethal cancer of the female reproductive organs. There are $5$ major histological subtypes of epithelial ovarian cancer, each with distinct morphological, genetic, and clinical features. Currently, these histotypes are determined by a pathologist's microscopic examination of tumor whole-slide images (WSI). This process has been hampered by poor inter-observer agreement (Cohen's kappa $0.54$-$0.67$). We utilized a \textit{two}-stage deep transfer learning algorithm based on convolutional neural networks (CNN) and progressive resizing for automatic classification of ep…
MICS Laboratory, CentraleSupélec, Université Paris-Saclay（巴黎萨克雷大学中央理工高等电力学院MICS实验室）；Department of Radiotherapy, Gustave Roussy Cancer Campus（古斯塔夫·鲁西癌症园区放射治疗科）；Therapanacea（Therapanacea公司）
The examination of histopathology images is considered to be the gold standard for the diagnosis and stratification of cancer patients. A key challenge in the analysis of such images is their size, which can run into the gigapixels and can require tedious screening by clinicians. With the recent advances in computational medicine, automatic tools have been proposed to assist clinicians in their everyday practice. Such tools typically process these large images by slicing them into tiles that can then be encoded and utilized for different clinical models. In this study, we propose a novel gener…
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…
Recent breakthroughs in object detection and image classification using Convolutional Neural Networks (CNNs) are revolutionizing the state of the art in medical imaging, and microscopy in particular presents abundant opportunities for computer vision algorithms to assist medical professionals in diagnosis of diseases ranging from malaria to cancer. High resolution scans of microscopy slides called Whole Slide Images (WSIs) offer enough information for a cancer pathologist to come to a conclusion regarding cancer presence, subtype, and severity based on measurements of features within the slide…
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…
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…
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 …
