Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance with dense pixel-level annotations. However, drawing such labels on the giga-pixel whole slide images is extremely expensive and time-consuming. In this paper, we use only patch-level classification labels to achieve tissue semantic segmentation on histopathology images, finally reducing the annotation efforts. We proposed a two-step model including a classification and a segmentation phases. In the classification phase, we proposed a CAM-based model to generate pseudo masks by patch-level labels. In the segmentation phase, we achieved tissue semantic segmentation by our proposed Multi-Layer Pseudo-Supervision. Several technical novelties have been proposed to reduce the information gap between pixel-level and patch-level annotations. As a part of this paper, we introduced a new weakly-supervised semantic segmentation (WSSS) dataset for lung adenocarcinoma (LUAD-HistoSeg). We conducted several experiments to evaluate our proposed model on two datasets. Our proposed model outperforms two state-of-the-art WSSS approaches. Note that we can achieve comparable quantitative and qualitative results with the fully-supervised model, with only around a 2\% gap for MIoU and FwIoU. By comparing with manual labeling, our model can greatly save the annotation time from hours to minutes. The source code is available at: \url{https://github.com/ChuHan89/WSSS-Tissue}.
Tsinghua Shenzhen International Graduate School（清华深圳国际研究生院）；Tsinghua University（清华大学）；Tencent AI Lab（腾讯人工智能实验室）；University of California, Los Angeles（加州大学洛杉矶分校）；The Chinese University of Hong Kong（香港中文大学）
Recently, deep neural networks have greatly advanced histopathology image segmentation but usually require abundant annotated data. However, due to the gigapixel scale of whole slide images and pathologists' heavy daily workload, obtaining pixel-level labels for supervised learning in clinical practice is often infeasible. Alternatively, weakly-supervised segmentation methods have been explored with less laborious image-level labels, but their performance is unsatisfactory due to the lack of dense supervision. Inspired by the recent success of self-supervised learning methods, we present a lab…
Taiwan AI Labs（台湾人工智能实验室）；National Taiwan University College of Medicine（台湾大学医学院）；Taipei Veterans General Hospital（台北荣民总医院）；Academia Sinica（中央研究院）
Delineation of cancerous regions in gigapixel whole slide images (WSIs) is a crucial diagnostic procedure in digital pathology. This process is time-consuming because of the large search space in the gigapixel WSIs, causing chances of omission and misinterpretation at indistinct tumor lesions. To tackle this, the development of an automated cancerous region segmentation method is imperative. We frame this issue as a modeling problem with partial label WSIs, where some cancerous regions may be misclassified as benign and vice versa, producing patches with noisy labels. To learn from these patch…
Radboud University Medical Center（拉德堡德大学医学中心）；Institute of Experimental Immunology, University of Zurich（苏黎世大学实验免疫学研究所）；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）
We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. Concentricpatches at multiple resolutions with different fields of view are used to feed different branches of HookNet, and intermediate representations are combined via a hooking mechanism. We describe a framework to design and train HookNet for achieving high-resolution semantic segmentation and introduce constraints to guarantee pixel-wise alignment in feature maps during hooking. We show the advan…
Radboud University Medical Center（拉德堡德大学医学中心）；Ospedale Cannizzaro（坎尼扎罗医院）；Leids Universitair Medisch Centrum（莱顿大学医学中心）；University of Bayreuth（拜罗伊特大学）；Linköping University（林雪平大学）
Artificial Intelligence (AI) can potentially support histopathologists in the diagnosis of a broad spectrum of cancer types. In colorectal cancer (CRC), AI can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs, ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in automated assessment of CRC histopathology whole-slide images. First, we present an AI-based method to segment multiple tissue compartments…
Learning discriminative representations of unlabelled data is a challenging task. Contrastive self-supervised learning provides a framework to learn meaningful representations using learned notions of similarity measures from simple pretext tasks. In this work, we propose a simple and efficient framework for self-supervised image segmentation using contrastive learning on image patches, without using explicit pretext tasks or any further labeled fine-tuning. A fully convolutional neural network (FCNN) is trained in a self-supervised manner to discern features in the input images and obtain con…
University Hospital Essen (AöR)（埃森大学医院）；Cancer Research Center Cologne Essen (CCCE)（科隆-埃森癌症研究中心）；West German Cancer Center Essen（埃森西德癌症中心）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；Hannover Medical School（汉诺威医学院）
The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely. The identification of subtypes of tumour tissue complicates the issue as the sharpness of separation decreases and the pathologist's reasoning is even more guided by spatial context. However, the identification of detailed types of tissue is crucial for providing personalized cancer therapies. Due to the high resolution of whole slide images, existing sem…
Semantic Segmentation Based Quality Control of Histopathology Whole Slide Images
Indian Institute of Technology - Bombay（印度理工学院-博伊斯班加分校）；Department of Electrical Engineering（电气工程系）；Koita Centre for Digital Health（数字健康中心）；Indian Council of Medical Research（印度医学研究委员会）；National Institute for Research in Digital Health and Data Science（数字健康与数据科学国家研究所）；Tata Memorial Centre – ACTREC（塔塔纪念中心–ACTREC）；HBNI
University of Washington（华盛顿大学）；Seattle Children’s Hospital（西雅图儿童医院）；University of Vermont（佛蒙特大学）；Pathology Associates
In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification. Based on recent advancements in instance segmentation and the Mask R-CNN model, our duct-level segmenter tries to identify each ductal individual inside a microscopic image; then, it extracts tissue-level information from the identified ductal instances. Leveraging three levels of information obtained from these ductal instances and also the histopathology image, the…
Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）；Guilin University of Electronic Technology（桂林电子科技大学）；Guangdong Provicial People’s Hospital Ganzhou Hospital（广东省人民医院赣州医院）
Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outs…
Can We Go Beyond Visual Features? Neural Tissue Relation Modeling for Relational Graph Analysis in Non-Melanoma Skin Histology
Mohamed bin Zayed University of AI（Mohamed bin Zayed人工智能大学）；School of Information and Data Sciences（信息与数据科学学院）；Vellore Institute of Technology（维洛雷理工学院）；Loughborough University（洛桑大学）；MD Anderson Cancer Center, The University of Texas（MD安德森癌症中心，德克萨斯大学）
Northeastern University（东北大学）；MBIE College（MBIE学院）；Engineering Research Center of Medical Imaging and Intelligent Analysis, Ministry of Education（教育部医学影像与智能分析工程研究中心）；Microscopic Image and Medical Image Analysis Group（显微图像与医学图像分析组）
For the Convolutional Neural Networks (CNNs) applied in the intelligent diagnosis of gastric cancer, existing methods mostly focus on individual characteristics or network frameworks without a policy to depict the integral information. Mainly, Conditional Random Field (CRF), an efficient and stable algorithm for analyzing images containing complicated contents, can characterize spatial relation in images. In this paper, a novel Hierarchical Conditional Random Field (HCRF) based Gastric Histopathology Image Segmentation (GHIS) method is proposed, which can automatically localize abnormal (cance…
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…
Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection, pathologists often zoom out to understand the tissue-level structures and zoom in to classify cells based on their morphology and the surrounding context. However, there is a lack of efforts to reflect such behaviors by pathologists in the cell detection models, mainly due to the lack of datasets containing both cell and tissue annotations with overlapping regions. To overcome this limitation, we propose and publicl…
Vector Institute（向量研究所）；University of Waterloo（滑铁卢大学）
Whole slide imaging (WSI) refers to the digitization of a tissue specimen which enables pathologists to explore high-resolution images on a monitor rather than through a microscope. The formation of tissue folds occur during tissue processing. Their presence may not only cause out-of-focus digitization but can also negatively affect the diagnosis in some cases. In this paper, we have compared five pre-trained convolutional neural networks (CNNs) of different depths as feature extractors to characterize tissue folds. We have also explored common classifiers to discriminate folded tissue against…
University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）；Vector Institute（矢量研究院）
Recent advances in digital pathology have led to the need for Histopathology Image Retrieval (HIR) systems that search through databases of biopsy images to find similar cases to a given query image. These HIR systems allow pathologists to effortlessly and efficiently access thousands of previously diagnosed cases in order to exploit the knowledge in the corresponding pathology reports. Since HIR systems may have to deal with millions of gigapixel images, the extraction of compact and expressive image features must be available to allow for efficient and accurate retrieval. In this paper, we p…
Isfahan University of Technology（伊斯法罕理工大学）；McMaster University（麦克马斯特大学）
Digital pathology is one of the most significant developments in modern medicine. Pathological examinations are the gold standard of medical protocols and play a fundamental role in diagnosis. Recently, with the advent of digital scanners, tissue histopathology slides can now be digitized and stored as digital images. As a result, digitized histopathological tissues can be used in computer-aided image analysis programs and machine learning techniques. Detection and segmentation of nuclei are some of the essential steps in the diagnosis of cancers. Recently, deep learning has been used for nucl…
In recent years, computational pathology has seen tremendous progress driven by deep learning methods in segmentation and classification tasks aiding prognostic and diagnostic settings. Nuclei segmentation, for instance, is an important task for diagnosing different cancers. However, training deep learning models for nuclei segmentation requires large amounts of annotated data, which is expensive to collect and label. This necessitates explorations into generative modeling of histopathological images. In this work, we use recent advances in conditional diffusion modeling to formulate a first-o…
A tissue and cell-level annotated H&E and PD-L1 histopathology image dataset in non-small cell lung cancer
Radboud University Medical Center（拉德堡德大学医学中心）；University of Brescia（布雷西亚大学）；Ospedale Sacro Cuore（圣十字医院）；Provincial Hospital of Bolzano (SABES-ASDAA)（博尔扎诺省医院）；University and Hospital Trust of Verona（威尼斯大学与医院信托）；Karolinska University Hospital（卡罗林斯卡大学医院）；Biopticka Laboratory, Ltd（Biopticka实验室）
University of Warwick（华威大学）；Tissue Image Analytics (TIA) Centre, University of Warwick（华威大学组织图像分析中心）
The problem of recognizing various types of tissues present in multi-gigapixel histology images is an important fundamental pre-requisite for downstream analysis of the tumor microenvironment in a bottom-up analysis paradigm for computational pathology. In this paper, we propose a deep dictionary learning approach to solve the problem of tissue phenotyping in histology images. We propose deep Multi-Resolution Dictionary Learning (deepMRDL) in order to benefit from deep texture descriptors at multiple different spatial resolutions. We show the efficacy of the proposed approach through extensive…
University of British Columbia（不列颠哥伦比亚大学）；Eindhoven University of Technology（埃因霍温理工大学）；Massachusetts General Hospital（马萨诸塞州综合医院）；Harvard Medical School（哈佛医学院）；BC Cancer Agency（不列颠哥伦比亚癌症机构）
Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning methods have made significant advances in the analysis and classification of tissue images. However, there has been limited work on the utility of such models in generating histopathology images. These synthetic images have several applications in pathology including utilities in education, proficiency testing, privacy, and data sharing. Recently, diffusion probabilistic models were introduced to generate high quality ima…
The diagnosis of cancer is mainly performed by visual analysis of the pathologists, through examining the morphology of the tissue slices and the spatial arrangement of the cells. If the microscopic image of a specimen is not stained, it will look colorless and textured. Therefore, chemical staining is required to create contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, cutting thicknesses, and laboratory protocols, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to…
Vector Institute（向量研究所）；Pierre and Marie Curie University（皮埃尔和玛丽·居里大学）；Sorbonne University（索邦大学）；University of Waterloo（滑铁卢大学）
Whole Slide Imaging (WSI) has become an important topic during the last decade. Even though significant progress in both medical image processing and computational resources has been achieved, there are still problems in WSI that need to be solved. A major challenge is the scan size. The dimensions of digitized tissue samples may exceed 100,000 by 100,000 pixels causing memory and efficiency obstacles for real-time processing. The main contribution of this work is representing a WSI by selecting a small number of patches for algorithmic processing (e.g., indexing and search). As a result, we r…
GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology
School of Computer Science and Engineering（计算机科学与工程学院）；University of New South Wales（新南威尔士大学）；St. George and Sutherland Clinical School（圣乔治与萨瑟兰临床学院）；Department of Anatomical Pathology（解剖病理学部）；NSW Health Pathology（新南威尔士州卫生病理学）；Cancer Care Centre（癌症护理中心）
Staining normalization in histopathology: Method benchmarking using multicenter dataset
University of Turku, Institute of Biomedicine（图尔库大学生物医学研究所）；Department of Pathology, Hospital Nova of Central Finland（中央芬兰医院病理科部）；University of Eastern Finland, Institute of Biomedicine（东部芬兰大学生物医学研究所）；Tampere University, Faculty of Medicine and Health Technology（塔皮奥拉大学医学与健康技术学院）；InFlames Research Flagship, University of Turku（InFlames研究旗舰，图尔库大学）
Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis. It can spare men with a normal exam from undergoing invasive biopsy while making biopsies more accurate in men with lesions suspicious for cancer. Yet, the subtle differences between cancer and confounding conditions, render the interpretation of MRI challenging. The tissue collected from patients that undergo pre-surgical MRI and radical prostatectomy provides a unique opportunity to correlate histopathology images of the entire prostate with MRI in order to accurately map the extent of prostate cancer …
Mohs micrographic surgery (MMS) is a precise oncological technique where layers of tissue are resected and examined with intraoperative histopathology to minimize the removal of normal tissue while completely excising the cancer. To achieve intraoperative pathology, the tissue is frozen, sectioned and stained over a 20- to 60-minute period, then analyzed by the MMS surgeon. Surgery is continued one layer at a time until no cancerous cells remain, meaning MMS can take several hours to complete. Ideally, it would be desirable to circumvent or augment frozen sectioning methods and directly visual…
The Pennsylvania State University（宾夕法尼亚州立大学）；Lister Hill National Center for Biomedical Communications（利斯特希尔国家生物医学通信中心）；National Library of Medicine（国家医学图书馆）；Penn State Health Milton S. Hershey Medical Center（宾夕法尼亚州立大学健康米尔顿·S·赫尔希医学中心）；Penn State College Of Medicine（宾夕法尼亚州立大学医学院）
Histopathological analysis is the present gold standard for precancerous lesion diagnosis. The goal of automated histopathological classification from digital images requires supervised training, which requires a large number of expert annotations that can be expensive and time-consuming to collect. Meanwhile, accurate classification of image patches cropped from whole-slide images is essential for standard sliding window based histopathology slide classification methods. To mitigate these issues, we propose a carefully designed conditional GAN model, namely HistoGAN, for synthesizing realisti…
Histopathology digital scans are large-size images that contain valuable information at the pixel level. Content-based comparison of these images is a challenging task. This study proposes a content-based similarity measure for high-resolution gigapixel histopathology images. The proposed similarity measure is an expansion of cosine vector similarity to a matrix. Each image is divided into same-size patches with a meaningful amount of information (i.e., contained enough tissue). The similarity is measured by the extraction of patch-level deep embeddings of the last pooling layer of a pre-train…
Due to the recent advancements in machine vision, digital pathology has gained significant attention. Histopathology images are distinctly rich in visual information. The tissue glass slide images are utilized for disease diagnosis. Researchers study many methods to process histopathology images and facilitate fast and reliable diagnosis; therefore, the availability of high-quality slides becomes paramount. The quality of the images can be negatively affected when the glass slides are ink-marked by pathologists to delineate regions of interest. As an example, in one of the largest public histo…
New York University（纽约大学）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）
The current study of cell architecture of inflammation in histopathology images commonly performed for diagnosis and research purposes excludes a lot of information available on the biopsy slide. In autoimmune diseases, major outstanding research questions remain regarding which cell types participate in inflammation at the tissue level, and how they interact with each other. While these questions can be partially answered using traditional methods, artificial intelligence approaches for segmentation and classification provide a much more efficient method to understand the architecture of infl…
During the last decade, the digitization of pathology has gained considerable momentum. Digital pathology offers many advantages including more efficient workflows, easier collaboration as well as a powerful venue for telepathology. At the same time, applying Computer-Aided Diagnosis (CAD) on Whole Slide Images (WSIs) has received substantial attention as a direct result of the digitization. The first step in any image analysis is to extract the tissue. Hence, background removal is an essential prerequisite for efficient and accurate results for many algorithms. In spite of the obvious discrim…
The distribution and appearance of nuclei are essential markers for the diagnosis and study of cancer. Despite the importance of nuclear morphology, there is a lack of large scale, accurate, publicly accessible nucleus segmentation data. To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process. We have generated nucleus segmentation results in 5,060 Whole Slide Tissue images from 10 cancer types in The Cancer Genome Atlas. One key component of our work is that we carried out a multi-level qual…
North Shore Country Day（北岸乡村日校）；Northwestern University（西北大学）
Data labeling is often the most challenging task when developing computational pathology models. Pathologist participation is necessary to generate accurate labels, and the limitations on pathologist time and demand for large, labeled datasets has led to research in areas including weakly supervised learning using patient-level labels, machine assisted annotation and active learning. In this paper we explore self-supervised learning to reduce labeling burdens in computational pathology. We explore this in the context of classification of breast cancer tissue using the Barlow Twins approach, an…
University of Toronto（多伦多大学）；National Cancer Research Centre（国家癌症研究中心）；Institute for Oncology and Radiology（肿瘤与放射学研究所）
Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue proliferative lesions. In this paper, we propose a practical and self-interpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in unsupervised manner and generates a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patter…
DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology
University of Glasgow（格拉斯哥大学）；Dotphoton；Fraunhofer HHI（弗劳恩霍夫海因里希·赫兹研究所）；Aignostics；Institute of Pathology, LMU Munich（慕尼黑大学病理学研究所）；UC Berkeley（加州大学伯克利分校）；TU Berlin（柏林工业大学）
Pennsylvania State University（宾夕法尼亚州立大学）；National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）
Cervical intraepithelial neoplasia (CIN) grade of histopathology images is a crucial indicator in cervical biopsy results. Accurate CIN grading of epithelium regions helps pathologists with precancerous lesion diagnosis and treatment planning. Although an automated CIN grading system has been desired, supervised training of such a system would require a large amount of expert annotations, which are expensive and time-consuming to collect. In this paper, we investigate the CIN grade classification problem on segmented epithelium patches. We propose to use conditional Generative Adversarial Netw…
Inception Institute of Artificial Intelligence（起源人工智能研究院）；EPFL（洛桑联邦理工学院）；Lausanne University Hospital（洛桑大学医院）；Center of Biomedical Imaging（生物医学成像中心）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural information of tissues. We propose a self-supervised approach to incorporate semantic guidance into a GAN based stain normalization framework and preserve detailed structural information. Our method does not require manual segmentation maps which is a significant advantage over existing methods. We integrate semantic information at different layers between a pre-trained semantic network and the stain color normalization …
Domain shift is a problem commonly encountered when developing automated histopathology pipelines. The performance of machine learning models such as convolutional neural networks within automated histopathology pipelines is often diminished when applying them to novel data domains due to factors arising from differing staining and scanning protocols. The Dual-Channel Auto-Encoder (DCAE) model was previously shown to produce feature representations that are less sensitive to appearance variation introduced by different digital slide scanners. In this work, the Multi-Channel Auto-Encoder (MCAE)…
AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology
Department of Computer Science and Software Engineering (CSSE)（计算机科学与软件工程系）；Concordia University（Concordia大学）；Concordia Institute for Information Systems Engineering (CIISE)（Concordia信息系统工程研究所）；Department of Building, Civil, and Environmental Engineering（建筑、土木和环境工程系）；University of Montreal Hospital Center (CHUM)（蒙特利尔大学医院中心）；Mila–Quebec AI Institute（Mila-魁北克人工智能研究所）；Khalifa University（Khalifa大学）；McGill University（麦吉尔大学）；Institute for Research in Immunology and Cancer（免疫学与癌症研究所在）
RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency
Stony Brook University（石英溪大学）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（阿提诺拉A.马丁努斯生物医学影像中心，麻省总医院和哈佛医学院）；Department of Biomedical Data Science, Stanford University（生物医学数据科学系，斯坦福大学）；Department of Pathology and Cell Biology, Columbia University（病理学与细胞生物学系，哥伦比亚大学）
New York University（纽约大学）；IIIT Hyderabad（印度海得拉巴国际信息技术研究所）
Systemic lupus erythematosus (SLE) is an autoimmune disease in which the immune system of the patient starts attacking healthy tissues of the body. Lupus Nephritis (LN) refers to the inflammation of kidney tissues resulting in renal failure due to these attacks. The International Society of Nephrology/Renal Pathology Society (ISN/RPS) has released a classification system based on various patterns observed during renal injury in SLE. Traditional methods require meticulous pathological assessment of the renal biopsy and are time-consuming. Recently, computational techniques have helped to allevi…
Helmholtz Munich（亥姆霍兹慕尼黑中心）；Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
In many histopathology tasks, sample classification depends on morphological details in tissue or single cells that are only visible at the highest magnification. For a pathologist, this implies tedious zooming in and out, while for a computational decision support algorithm, it leads to the analysis of a huge number of small image patches per whole slide image (WSI). Attention-based multiple instance learning (MIL), where attention estimation is learned in a weakly supervised manner, has been successfully applied in computational histopathology, but it is challenged by large numbers of irrele…
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…
It is commonly recognized that color variations caused by differences in stains is a critical issue for histopathology image analysis. Existing methods adopt color matching, stain separation, stain transfer or the combination of them to alleviate the stain variation problem. In this paper, we propose a novel Stain-Adaptive Self-Supervised Learning(SASSL) method for histopathology image analysis. Our SASSL integrates a domain-adversarial training module into the SSL framework to learn distinctive features that are robust to both various transformations and stain variations. The proposed SASSL i…
Stony Brook Medicine（石溪大学医学中心）；Stony Brook University（石溪大学）；Cold Spring Harbor Laboratory（冷泉港实验室）
Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for various tasks, including immune cell detection and microsatellite instability classification. The state-of-the-art for each task often employs base architectures that have been pretrained for image classification on ImageNet. The standard approach to develop classifiers in histopathology tends to focus narrowly on optimizing models for a single task, not considering the aspects of modeling innovatio…
Joint analysis of multiple biomarker images and tissue morphology is important for disease diagnosis, treatment planning and drug development. It requires cross-staining comparison among Whole Slide Images (WSIs) of immuno-histochemical and hematoxylin and eosin (H&E) microscopic slides. However, automatic, and fast cross-staining alignment of enormous gigapixel WSIs at single-cell precision is challenging. In addition to morphological deformations introduced during slide preparation, there are large variations in cell appearance and tissue morphology across different staining. In this paper, …
Cancer is the uncontrollable cell division of abnormal cells inside the human body, which can spread to other body organs. It is one of the non-communicable diseases (NCDs) and NCDs accounts for 71% of total deaths worldwide whereas lung cancer is the second most diagnosed cancer after female breast cancer. Cancer survival rate of lung cancer is only 19%. There are various methods for the diagnosis of lung cancer, such as X-ray, CT scan, PET-CT scan, bronchoscopy and biopsy. However, to know the subtype of lung cancer based on the tissue type H and E staining is widely used, where the staining…
Kyushu University（九州大学）；Kyoto University Hospital（京都大学医院）
Pathological diagnosis is used for examining cancer in detail, and its automation is in demand. To automatically segment each cancer area, a patch-based approach is usually used since a Whole Slide Image (WSI) is huge. However, this approach loses the global information needed to distinguish between classes. In this paper, we utilized the Distance from the Boundary of tissue (DfB), which is global information that can be extracted from the original image. We experimentally applied our method to the three-class classification of cervical cancer, and found that it improved the total performance …
