Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge
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）
Mostafa Jahanifar, Adam Shephard, Neda Zamani Tajeddin, R. M. Saad Bashir, Mohsin Bilal, Syed Ali Khurram, Fayyaz Minhas, Nasir Rajpoot
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. Cross-validation on the training images achieved the F1-score of 0.786 and 0.765 on the preliminary test set, demonstrating the generalizability of our model to unseen data from new scanners.
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Harbin Institute of Technology（哈尔滨工业大学）
The account of mitotic cells is a key feature in tumor diagnosis. However, due to the variability of mitotic cell morphology, it is a highly challenging task to detect mitotic cells in tumor tissues. At the same time, although advanced deep learning method have achieved great success in cell detection, the performance is often unsatisfactory when tested data from another domain (i.e. the different tumor types and different scanners). Therefore, it is necessary to develop algorithms for detecting mitotic cells with robustness in domain shifts scenarios. Our work further proposes a foreground de…
Multispectral Spatial Characterization: Application to Mitosis Detection in Breast Cancer Histopathology
University Joseph Fourier（约瑟夫·傅里叶大学）；IPAL, CNRS UMI 2955（IPAL，法国国家科学研究中心UMI 2955）；CoSMo Software（CoSMo软件公司）；Temasys Communications（Temasys通信公司）；University Pierre and Marie Curie（皮埃尔和玛丽·居里大学）
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…
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）
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…
IBM Research Zurich（IBM苏黎世研究院）；ETH Zurich（苏黎世联邦理工学院）
Mitotic counting is a vital prognostic marker of tumor proliferation in breast cancer. Deep learning-based mitotic detection is on par with pathologists, but it requires large labeled data for training. We propose a deep classification framework for enhancing mitosis detection by leveraging class label information, via softmax loss, and spatial distribution information among samples, via distance metric learning. We also investigate strategies towards steadily providing informative samples to boost the learning. The efficacy of the proposed framework is established through evaluation on ICPR 2…
Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation
South China University of Technology（华南理工大学）；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）；Nanfang Hospital（南方医院）；Guangdong Provincial People’s Hospital Ganzhou Hospital（广东省人民医院赣州医院）；Guilin University of Electronic Technology（桂林电子科技大学）
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…
Guilin University of Electronic Technology（桂林电子科技大学）；Guangxi Key Laboratory of Image and Graphic Intelligent Processing（广西图像图形智能处理重点实验室）；Ganzhou Municipal Hospital（赣州市立医院）
Mitosis detection is one of the challenging problems in computational pathology, and mitotic count is an important index of cancer grading for pathologists. However, current counts of mitotic nuclei rely on pathologists looking microscopically at the number of mitotic nuclei in hot spots, which is subjective and time-consuming. In this paper, we propose a two-stage cascaded network, named FoCasNet, for mitosis detection. In the first stage, a detection network named M_det is proposed to detect as many mitoses as possible. In the second stage, a classification network M_class is proposed to ref…
The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade. Automated mitosis detection through deep learning-oriented image analysis often fails on unseen patient data due to inherent domain biases. This paper proposes a domain homogenizer for mitosis detection that attempts to alleviate domain differences in histology images via adversarial reconstruction of input images. The proposed homogenizer is based on a U-Net architecture and can effectively reduce domain differences commonly seen with histology imaging data. We demonstrate our domain ho…
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…
For the MIDOG mitosis detection challenge, we created a cascade algorithm consisting of a Mask-RCNN detector, followed by a classification ensemble consisting of ResNet50 and DenseNet201 to refine detected mitotic candidates. The MIDOG training data consists of 200 frames originating from four scanners, three of which are annotated for mitotic instances with centroid annotations. Our main algorithmic choices are as follows: first, to enhance the generalizability of our detector and classification networks, we use a state-of-the-art residual Cycle-GAN to transform each scanner domain to every o…
Universitat Politècnica de València（瓦伦西亚理工大学）；Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）
Mitotic activity is a crucial proliferation biomarker for the diagnosis and prognosis of different types of cancers. Nevertheless, mitosis counting is a cumbersome process for pathologists, prone to low reproducibility, due to the large size of augmented biopsy slides, the low density of mitotic cells, and pattern heterogeneity. To improve reproducibility, deep learning methods have been proposed in the last years using convolutional neural networks. However, these methods have been hindered by the process of data labelling, which usually solely consist of the mitosis centroids. Therefore, cur…
Shiraz University of Technology（设拉子理工大学）；National Brain Mapping Laboratory（国家脑图谱实验室）
We propose a two-step domain shift-invariant mitosis cell detection method based on Faster RCNN and a convolutional neural network (CNN). We generate various domain-shifted versions of existing histopathology images using a stain augmentation technique, enabling our method to effectively learn various stain domains and achieve better generalization. The performance of our method is evaluated on the preliminary test data set of the MIDOG-2021 challenge. The experimental results demonstrate that the proposed mitosis detection method can achieve promising performance for domain-shifted histopatho…
Chulalongkorn University（朱拉隆功大学）；King Chulalongkorn Memorial Hospital（朱拉隆功国王纪念医院）；NVIDIA AI Technology Center（英伟达人工智能技术中心）
Mitotic count (MC) is an important histological parameter for cancer diagnosis and grading, but the manual process for obtaining MC from whole-slide histopathological images is very time-consuming and prone to error. Therefore, deep learning models have been proposed to facilitate this process. Existing approaches utilize a two-stage pipeline: the detection stage for identifying the locations of potential mitotic cells and the classification stage for refining prediction confidences. However, this pipeline formulation can lead to inconsistencies in the classification stage due to the poor pred…
Sichuan University（四川大学）；Tencent AI Lab（腾讯AI实验室）；Tsinghua University（清华大学）
Mitotic count is the most important morphological feature of breast cancer grading. Many deep learning-based methods have been proposed but suffer from domain shift. In this work, we construct a Fourier-based segmentation model for mitosis detection to address the problem. Swapping the low-frequency spectrum of source and target images is shown effective to alleviate the discrepancy between different scanners. Our Fourier-based segmentation method can achieve F1 with 0.7456 on the preliminary test set.
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…
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…
University Hospital and University of Zurich（苏黎世大学医院及苏黎世大学）
Automated detection of mitotic figures in histopathology images is a challenging task: here, we present the different steps that describe the strategy we applied to participate in the MIDOG 2021 competition. The purpose of the competition was to evaluate the generalization of solutions to images acquired with unseen target scanners (hidden for the participants) under the constraint of using training data from a limited set of four independent source scanners. Given this goal and constraints, we joined the challenge by proposing a straight-forward solution based on a combination of state-of-the…
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）
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）
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）
University of Memphis（孟菲斯大学）；Microsoft Research（微软研究院）；AutoML Image Lab（AutoML图像实验室）
Breast Cancer is the most prevalent cancer in the world. The World Health Organization reports that the disease still affects a significant portion of the developing world citing increased mortality rates in the majority of low to middle income countries. The most popular protocol pathologists use for diagnosing breast cancer is the Nottingham grading system which grades the proliferation of tumors based on 3 major criteria, the most important of them being mitotic cell count. The way in which pathologists evaluate mitotic cell count is to subjectively and qualitatively analyze cells present i…
Centre for Machine Intelligence and Data Science, IIT Bombay, India（机器智能与数据科学中心，IIT博因达，印度）；Department of Electrical Engineering, IIT Bombay, India（电气工程系，IIT博因达，印度）
针对跨扫描仪、染色与采集域偏移下的非典型有丝分裂分类，本文提出风格扰动、弱标签特征对齐与 EMA 教师蒸馏相结合的训练方案，在 MIDOG 2025 初步榜单取得均衡高性能。
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）
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）
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…
Each woman living in the United States has about 1 in 8 chance of developing invasive breast cancer. The mitotic cell count is one of the most common tests to assess the aggressiveness or grade of breast cancer. In this prognosis, histopathology images must be examined by a pathologist using high-resolution microscopes to count the cells. Unfortunately, this can be an exhaustive task with poor reproducibility, especially for non-experts. Deep learning networks have recently been adapted to medical applications which are able to automatically localize these regions of interest. However, these r…
Automated detection of mitotic figures in histopathology images has seen vast improvements, thanks to modern deep learning-based pipelines. Application of these methods, however, is in practice limited by strong variability of images between labs. This results in a domain shift of the images, which causes a performance drop of the models. Hypothesizing that the scanner device plays a decisive role in this effect, we evaluated the susceptibility of a standard mitosis detection approach to the domain shift introduced by using a different whole slide scanner. Our work is based on the MICCAI-MIDOG…
University Hospital and University of Zurich（苏黎世大学医院和苏黎世大学）
Making histopathology image classifiers robust to a wide range of real-world variability is a challenging task. Here, we describe a candidate deep learning solution for the Mitosis Domain Generalization Challenge 2022 (MIDOG) to address the problem of generalization for mitosis detection in images of hematoxylin-eosin-stained histology slides under high variability (scanner, tissue type and species variability). Our approach consists in training a rotation-invariant deep learning model using aggressive data augmentation with a training set enriched with hard negative examples and automatically…
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…
Image Analysis in Medicine Lab (IAMLAB), Electrical, Computer and Biomedical Engineering, Ryerson University（Ryerson大学电气、计算机与生物医学工程学院医学影像分析实验室（IAMLAB））；Princess Margaret Cancer Centre, University Health Network（玛格丽特公主癌症中心（大学健康网络））；Department of Laboratory Medicine and Pathobiology, University of Toronto（多伦多大学检验医学与病理生物学系）
Mitotic counts are one of the key indicators of breast cancer prognosis. However, accurate mitotic cell counting is still a difficult problem and is labourious. Automated methods have been proposed for this task, but are usually dependent on the training images and show poor performance on unseen domains. In this work, we present a multi-stage mitosis detection method based on a Cascade RCNN developed to be sequentially more selective against false positives. On the preliminary test set, the algorithm scores an F1-score of 0.7492.
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…
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…
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）
Normal and Atypical Mitosis Image Classifier using Efficient Vision Transformer
Laboratory of Cancer Biology and Genetics, NCI, NIH, Bethesda, MD, 20852, USA（癌症生物学与遗传学实验室，国家癌症研究所，美国国家卫生研究院，贝塞斯达，马里兰州，20852，美国）；Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27705, USA（放射肿瘤学系，杜克大学医学中心，达勒姆，北卡罗来纳州，27705，美国）；University of Hawaii Cancer Center, Honolulu, HI, 96813, USA（夏威夷大学癌症中心，霍洛汉，夏威夷州，96813，美国）
