How Many Annotators Do We Need? -- A Study on the Influence of Inter-Observer Variability on the Reliability of Automatic Mitotic Figure Assessment
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Freie Universität Berlin（柏林自由大学）；Animal Medical Center（动物医疗中心）；University of Pennsylvania（宾夕法尼亚大学）；University of Veterinary Medicine Hannover（汉诺威兽医大学）；Synlab’s VPG Histology（Synlab VPG组织学部门）；Michigan State University（密歇根州立大学）；Idorsia Pharmaceuticals Ltd（Idorsia制药有限公司）；International Zoo Veterinary Group（国际动物园兽医集团）；University of Bern（伯尔尼大学）；IDEXX Vet Med Labor GmbH（爱德士兽医实验室有限责任公司）
Frauke Wilm, Christof A. Bertram, Christian Marzahl, Alexander Bartel, Taryn A. Donovan, Charles-Antoine Assenmacher, Kathrin Becker, Mark Bennett, Sarah Corner… 展开作者
Frauke Wilm, Christof A. Bertram, Christian Marzahl, Alexander Bartel, Taryn A. Donovan, Charles-Antoine Assenmacher, Kathrin Becker, Mark Bennett, Sarah Corner, Brieuc Cossic, Daniela Denk, Martina Dettwiler, Beatriz Garcia Gonzalez, Corinne Gurtner, Annika Lehmbecker, Sophie Merz, Stephanie Plog, Anja Schmidt, Rebecca C. Smedley, Marco Tecilla, Tuddow Thaiwong, Katharina Breininger, Matti Kiupel, Andreas Maier, Robert Klopfleisch, Marc Aubreville
备注：
Due to data inconsistencies experiments had to be repeated with a reduced number of annotators (17 in version 1). All findings of the previous version were reproducible. 7 pages, 2 figures, accepted at BVM workshop 2021
Density of mitotic figures in histologic sections is a prognostically relevant characteristic for many tumours. Due to high inter-pathologist variability, deep learning-based algorithms are a promising solution to improve tumour prognostication. Pathologists are the gold standard for database development, however, labelling errors may hamper development of accurate algorithms. In the present work we evaluated the benefit of multi-expert consensus (n = 3, 5, 7, 9, 11) on algorithmic performance. While training with individual databases resulted in highly variable F$_1$ scores, performance was notably increased and more consistent when using the consensus of three annotators. Adding more annotators only resulted in minor improvements. We conclude that databases by few pathologists and high label accuracy may be the best compromise between high algorithmic performance and time investment.
Korea Advanced Institute of Science and Technology（韩国高等科学技术院）
We propose a new training scheme for domain generalization in mitotic figure detection. Mitotic figures show different characteristics for each scanner. We consider each scanner as a 'domain' and the image distribution specified for each domain as 'style'. The goal is to train our network to be robust on scanner types by using various 'style' images. To expand the style variance, we transfer a style of the training image into arbitrary styles, by defining a module based on StarGAN. Our model with the proposed training scheme shows positive performance on MIDOG Preliminary Test-Set containing s…
Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK（华威大学计算机科学系组织图像分析中心，英国）；School of Clinical Dentistry, University of Sheffield, Sheffield, UK（谢菲尔德大学口腔临床学院，英国）
The detection of mitotic figures from different scanners/sites remains an important topic of research, owing to its potential in assisting clinicians with tumour grading. The MItosis DOmain Generalization (MIDOG) challenge aims to test the robustness of detection models on unseen data from multiple scanners for this task. We present a short summary of the approach employed by the TIA Centre team to address this challenge. Our approach is based on a hybrid detection model, where mitotic candidates are segmented on stain normalised images, before being refined by a deep learning classifier. Cros…
Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming, requiring a trained pathologist to manually examine histopathological images in order to identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a novel region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the thre…
Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification
Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；University of Veterinary Medicine, Vienna（维也纳兽医大学）；Center for AI and Data Science, Julius-Maximilians-Universität Würzburg（维尔茨堡大学朱利叶斯-马克西米利安人工智能与数据科学中心）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Flensburg University of Applied Sciences（弗伦斯堡应用技术大学）
Pattern Recognition Lab（模式识别实验室）；Computer Sciences（计算机科学（学院））；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Institute of Veterinary Pathology（兽医病理学研究所）；Freie Universität Berlin（柏林自由大学）；Institute of Neuropathology（神经病理学研究所）
For histopathological tumor assessment, the count of mitotic figures per area is an important part of prognostication. Algorithmic approaches - such as for mitotic figure identification - have significantly improved in recent times, potentially allowing for computer-augmented or fully automatic screening systems in the future. This trend is further supported by whole slide scanning microscopes becoming available in many pathology labs and could soon become a standard imaging tool. For an application in broader fields of such algorithms, the availability of mitotic figure data sets of sufficien…
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Freie Universität Berlin（柏林自由大学）
Histopathological prognostication of neoplasia including most tumor grading systems are based upon a number of criteria. Probably the most important is the number of mitotic figures which are most commonly determined as the mitotic count (MC), i.e. number of mitotic figures within 10 consecutive high power fields. Often the area with the highest mitotic activity is to be selected for the MC. However, since mitotic activity is not known in advance, an arbitrary choice of this region is considered one important cause for high variability in the prognostication and grading. In this work, we prese…
Classifying Mitotic Figures in the MIDOG25 Challenge with Deep Ensemble Learning and Rule Based Refinement
IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, University of Augsburg, Germany（转化医学研究信息基础设施，应用计算机科学学院，乌尔姆大学）；Department of Neuropathology, Pathology, Medical Faculty, University of Augsburg, Germany（神经病理学系，病理学，医学系，乌尔姆大学）；DigiHealth Institute, Neu-Ulm University of Applied Sciences, Germany（DigiHealth研究所，纽乌尔姆应用科学大学）；Institute of Medical Data Science, University Hospital Würzburg, Germany（医学数据科学研究所，乌尔姆大学医院）
Challenges and Lessons from MIDOG 2025: A Two-Stage Approach to Domain-Robust Mitotic Figure Detection
Department of Medicine, Korea University Graduate School（韩国大学研究生院医学系）；Department of Medicine, Dongguk University（东国大学医学系）；Department of Computer Science and Engineering, Sogang University（ソガン大学计算机科学与工程系）
Pan-Cancer mitotic figures detection and domain generalization: MIDOG 2025 Challenge
Department of Medical Physics and Biomedical Engineering, UCL, UK（医学物理与生物医学工程系，UCL，英国）；Department of Radiotherapy, University College London Hospitals, London, UK（放射治疗系，University College London Hospitals，伦敦，英国）；Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK（进化与癌症中心，癌症研究所在伦敦，英国）
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Freie Universität Berlin（柏林自由大学）
Most tumor grading systems for human as for veterinary histopathology are based upon the absolute count of mitotic figures in a certain reference area of a histology slide. Since time for prognostication is limited in a diagnostic setting, the pathologist will often almost arbitrarily choose a certain field of interest assumed to have the highest mitotic activity. However, as mitotic figures are commonly very sparse on the slide and often have a patchy distribution, this poses a sampling problem which is known to be able to influence the tumor prognostication. On the other hand, automatic dete…
Technische Hochschule Ingolstadt（因戈尔施塔特应用技术大学）；Schwarzman Animal Medical Center（施瓦茨曼动物医疗中心）；Tribun Health；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University of Veterinary Medicine Vienna（维也纳兽医大学）
Mitotic activity is key for the assessment of malignancy in many tumors. Moreover, it has been demonstrated that the proportion of abnormal mitosis to normal mitosis is of prognostic significance. Atypical mitotic figures (MF) can be identified morphologically as having segregation abnormalities of the chromatids. In this work, we perform, for the first time, automatic subtyping of mitotic figures into normal and atypical categories according to characteristic morphological appearances of the different phases of mitosis. Using the publicly available MIDOG21 and TUPAC16 breast cancer mitosis da…
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Freie Universität Berlin（柏林自由大学）；University of Bern（伯尔尼大学）；IDEXX Laboratories（艾迪克斯实验室）
Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section.We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (fi…
Freie Universität Berlin（柏林自由大学）；Eindhoven University of Technology（埃因霍温理工大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University Medical Center Utrecht（乌得勒支大学医学中心）
Pathologist-defined labels are the gold standard for histopathological data sets, regardless of well-known limitations in consistency for some tasks. To date, some datasets on mitotic figures are available and were used for development of promising deep learning-based algorithms. In order to assess robustness of those algorithms and reproducibility of their methods it is necessary to test on several independent datasets. The influence of different labeling methods of these available datasets is currently unknown. To tackle this, we present an alternative set of labels for the images of the aux…
RF-DETR for Robust Mitotic Figure Detection: A MIDOG 2025 Track 1 Approach
Wrocław University of Science and Technology（沃斯拉夫科学与技术大学）；Cancer Center Sp. z o. o.（癌症中心有限公司）；Hospital for Lung Diseases - Rebirth（肺病医院 - Rebirth）；Maria Sklodowska-Curie National Research Institute of Oncology（玛丽·斯克洛多夫斯卡国家癌症研究所）；Pathology Department（病理部门）；th Military Research Hospital in Bydgoszcz（比多日斯茨第10军科研医院）；Department of Tumor Pathology and Pathomorphology（肿瘤病理学与病理形态学部门）；Oncology Centre Prof. Franciszek Łukaszczyk Memorial Hospital（弗雷德里克·卢卡斯茨基纪念医院肿瘤中心）；Doctoral School of Medical and Health Sciences, Nicolaus Copernicus University in Toruń（医学与健康科学博士学校，尼古拉斯·哥白尼大学在托伦）；Department of Obstetrics, Gynaecology and Oncology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun（妇产科与肿瘤学部门，比多日斯茨学院，尼古拉斯·哥白尼大学在托伦）
Foundation Model-Driven Classification of Atypical Mitotic Figures with Domain-Aware Training Strategies
Wrocław University of Science and Technology（沃拉什大学科学与技术学院）；Cancer Center Sp. z o. o.（癌症中心有限公司）；Hospital for Lung Diseases - Rebirth（肺病医院-重生）；Maria Sklodowska-Curie National Research Institute of Oncology（玛丽·斯克洛多夫斯卡国家癌症研究院）；Pathology Department（病理部门）；th Military Research Hospital in Bydgoszcz（比多斯茨第10军科研医院）；Department of Tumor Pathology and Pathomorphology（肿瘤病理与组织病理学部门）；Oncology Centre Prof. Franciszek Łukaszczyk Memorial Hospital（弗雷德里克·卢卡斯茨基纪念医院肿瘤中心）；Doctoral School of Medical and Health Sciences, Nicolaus Copernicus University in Toruń（医学与健康科学博士学院，尼古拉斯·哥白尼大学图努恩分校）；Department of Obstetrics, Gynaecology and Oncology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun（妇产科与肿瘤学系，比多斯茨医学院，尼古拉斯·哥白尼大学图努恩分校）
OMG-Net: A Deep Learning Framework Deploying Segment Anything to Detect Pan-Cancer Mitotic Figures from Haematoxylin and Eosin-Stained Slides
University College London（伦敦大学学院）；University College London Hospitals NHS Foundation Trust（伦敦大学学院医院 NHS 基金会信托）；University College London Cancer Institute（伦敦大学学院癌症研究所）；Royal National Orthopaedic Hospital NHS Foundation Trust（皇家国立骨科医院 NHS 基金会信托）；University Hospital Basel（巴塞尔大学医院）；University of Basel（巴塞尔大学）；Rutgers Biomedical and Health Sciences（罗格斯生物医学与健康科学学院）；Rutgers University（罗格斯大学）；The Robert Jones and Agnes Hunt Orthopaedic Hospital（罗伯特·琼斯和艾格尼丝·亨特骨科医院）；Keele University（基尔大学）
Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation
Flensburg University of Applied Sciences（弗劳恩霍夫应用科技大学）；University of Veterinary Medicine, Vienna（维也纳兽医大学）；The Schwarzman Animal Medical Center（施瓦茨曼动物医疗中心）；Diffusely（扩散地）；Freie Universität Berlin（柏林自由大学）；Technische Hochschule Ingolstadt（因戈尔施塔特技术大学）；Medical University of Vienna（维也纳医学大学）；Julius-Maximilians-Universität Würzburg（维尔茨堡约瑟夫-马克斯姆-大学）
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…
Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures
Flensburg University of Applied Sciences（弗伦斯堡应用科学大学）；University of Veterinary Medicine, Vienna（维也纳兽医大学）；Freie Universität Berlin（柏林自由大学）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；Medical University of Vienna（维也纳医科大学）；Julius-Maximilians-Universität Würzburg（维尔zburg大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
Efficient Fine-Tuning of DINOv3 Pretrained on Natural Images for Atypical Mitotic Figure Classification
Mines Paris, PSL University, Center for Computational Biology（巴黎 Mines 大学计算生物学中心）；Mines Paris, PSL University, Center for Mathematical Morphology（巴黎 Mines 大学数学形态学中心）；Sanofi（桑福有限公司）；Institut Curie, PSL University（Curie 机构，巴黎 Mines 大学）；INSERM, U1331 Computational Oncology（INSERM，U1331 计算肿瘤学）；Department of Pathology, University Hospital of Nantes（南特大学医院病理学系）
Xidian University（西安电子科技大学）；Ocean University of China（中国海洋大学）
Mitotic figure count is an important marker of tumor proliferation and has been shown to be associated with patients' prognosis. Deep learning based mitotic figure detection methods have been utilized to automatically locate the cell in mitosis using hematoxylin \& eosin (H\&E) stained images. However, the model performance deteriorates due to the large variation of color tone and intensity in H\&E images. In this work, we proposed a two stage mitotic figure detection framework by fusing a detector and a deep ensemble classification model. To alleviate the impact of color variation in H\&E ima…
Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)
University of Veterinary Medicine, Vienna（维也纳兽医大学）；The Schwarzman Animal Medical Center（施瓦茨曼动物医疗中心）；Flensburg University of Applied Sciences（弗伦斯堡应用科学大学）；Freie Universität Berlin（柏林自由大学）；Medical University of Vienna（维也纳医科大学）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）
Breast cancer is the most commonly diagnosed cancer worldwide, with over two million new cases each year. During diagnostic tumour grading, pathologists manually count the number of dividing cells (mitotic figures) in biopsy or tumour resection specimens. Since the process is subjective and time-consuming, data-driven artificial intelligence (AI) methods have been developed to automatically detect mitotic figures. However, these methods often generalise poorly, with performance reduced by variations in tissue types, staining protocols, or the scanners used to digitise whole-slide images. Domai…
On the Value of PHH3 for Mitotic Figure Detection on H&E-stained Images
Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；Gestalt Diagnostics（格式塔诊断公司）；University of Veterinary Medicine, Vienna（维也纳兽医大学）；Freie Universität Berlin（柏林自由大学）；SeaWorld Yas Island（海洋世界亚斯岛）；Cornell University（康奈尔大学）；USAMV（克卢日-纳波卡兽医与医学大学）；Medical University of Vienna（维也纳医科大学）；Hoffmann-La Roche（罗氏公司）；Ross University School of Veterinary Medicine（罗斯大学兽医学院）；University of Florida（佛罗里达大学）；University Hospital Erlangen（埃尔兰根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；IDEXX Laboratories（爱德士实验室）；The Schwarzman Animal Medical Center（施瓦茨曼动物医疗中心）
Assessing the Mitotic Count has a known high degree of intra- and inter-rater variability. Computer-aided systems have proven to decrease this variability and reduce labeling time. These systems, however, are generally highly dependent on their training domain and show poor applicability to unseen domains. In histopathology, these domain shifts can result from various sources, including different slide scanning systems used to digitize histologic samples. The MItosis DOmain Generalization challenge focused on this specific domain shift for the task of mitotic figure detection. This work presen…
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）
Domain variability is a common bottle neck in developing generalisable algorithms for various medical applications. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a target representative feature space through unpaired image to image translation (CycleGAN). We comprehensively evaluate the performanceand usefulness by utilising the transformation to mitosis detection with candidate proposal and classification. This work presents a simple yet effective multi-step mitotic figure detection algorithm developed as a baseli…
Assessment of algorithms for mitosis detection in breast cancer histopathology images
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；Pathology Department, University Medical Center Utrecht（乌得勒支大学医学中心病理系）；BME Department, Case Western Reserve University（凯斯西储大学生物医学工程系）；MindLab, National University of Colombia（哥伦比亚国立大学思维实验室）；Department of Applied Mathematics and Computer Science, Technical University of Denmark（丹麦技术大学应用数学与计算机科学系）；IDSIA, USI-SUPSI（瑞士应用科技大学智能系统与数据分析研究所）；Department of Computer Engineering, Işık University（伊希克大学计算机工程系）；MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology（巴黎高等矿业学院PSL研究大学生物计算中心）；Institut Curie（居里研究所）；INSERM U900（法国国家健康与医学研究院U900）；Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology（台湾科技大学生物医学工程研究所）；Corporate R&D Center, Panasonic Healthcare Co., Ltd.（松下医疗保健公司企业研发中心）；School of Computing, Engineering and Physical Sciences, University of Central Lancashire（中央兰开夏大学计算、工程与物理科学学院）；I3S, UMR 7271 UNS-CNRS, Nice Sophia-Antipolis University（尼斯索菲亚安蒂波利斯大学I3S联合研究实验室）；CVSSP, University of Surrey（萨里大学视觉、声音和感知安全研究中心）；Department of Computer Science and Sheffield institute for Translational Medicine, University of Sheffield（谢菲尔德大学计算机科学与谢菲尔德转化医学研究所）；Department of Computer Science, University of Warwick（华威大学计算机系）；Department of Computer Science & Engineering, College of Engineering Qatar University（卡塔尔大学工程学院计算机科学与工程系）；The Michael Letcher Department of Cellular Pathology, Princess Alexandra Hospital NHS Trust（公主亚历山医院NHS信托迈克尔·莱切尔细胞病理学系）
Technische Hochschule Ingolstadt（英戈尔施塔特工业大学）；University Hospital Erlangen（埃尔朗根大学医院）；University of Veterinary Medicine Vienna（维也纳兽医大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）
Meningioma is one of the most prevalent brain tumors in adults. To determine its malignancy, it is graded by a pathologist into three grades according to WHO standards. This grade plays a decisive role in treatment, and yet may be subject to inter-rater discordance. In this work, we present and compare three approaches towards fully automatic meningioma grading from histology whole slide images. All approaches are following a two-stage paradigm, where we first identify a region of interest based on the detection of mitotic figures in the slide using a state-of-the-art object detection deep lea…
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡大学）
Mitotic figure detection is a challenging task in digital pathology that has a direct impact on therapeutic decisions. While automated methods often achieve acceptable results under laboratory conditions, they frequently fail in the clinical deployment phase. This problem can be mainly attributed to a phenomenon called domain shift. An important source of a domain shift is introduced by different microscopes and their camera systems, which noticeably change the color representation of digitized images. In this method description we present our submitted algorithm for the Mitosis Domain General…
Teacher-Student Model for Detecting and Classifying Mitosis in the MIDOG 2025 Challenge
Image Analysis in Medicine Lab (IAMLAB)（医学图像分析实验室）；Electrical, Computer and Biomedical Engineering（电气、计算机与生物医学工程）；Toronto Metropolitan University（多伦多 Metropolitan 大学）；Princess Margaret Cancer Centre（公主玛格丽特癌症中心）；University Health Network（大学健康网络）；Department of Laboratory Medicine and Pathobiology（实验室医学与病理学系）；University of Toronto（多伦多大学）；Vector Institute of Artificial Intelligence（人工智能向量研究所）
Technische Hochschule Ingolstadt（因戈尔施塔特应用技术大学）；UMC Utrecht（乌得勒支大学医学中心）；Freie Universität Berlin（柏林自由大学）；Radboud UMC Nijmegen（奈梅亨拉德堡德大学医学中心）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Schwarzman Animal Medical Center（施瓦茨曼动物医学中心）；University of Leeds（利兹大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）
The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitotic figures by pathologists is known to be subject to a strong inter-rater bias, which limits the prognostic value. State-of-the-art deep learning methods can support the expert in this assessment but are known to strongly deteriorate when applied in a different clinical environment than was used for training. One decisive component in the underlying domain shift has been identified as the variability caused by using d…
