Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation
College of Information Engineering, Shenzhen University（深圳大学信息工程学院）；Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University（深圳大学广东省智能信息处理重点实验室）；Histo Pathology Diagnostic Center, Shanghai（上海组织病理学诊断中心）；School of Computer Science, University of Nottingham（诺丁汉大学计算机学院）；School of Medicine, University of Nottingham（诺丁汉大学医学院）
Xianxu Hou, Jingxin Liu, Bolei Xu, Bozhi Liu, Xin Chen, Mohammad Ilyas, Ian Ellis, Jon Garibaldi, Guoping Qiu
Supervised semantic segmentation normally assumes the test data being in a similar data domain as the training data. However, in practice, the domain mismatch between the training and unseen data could lead to a significant performance drop. Obtaining accurate pixel-wise label for images in different domains is tedious and labor intensive, especially for histopathology images. In this paper, we propose a dual adaptive pyramid network (DAPNet) for histopathological gland segmentation adapting from one stain domain to another. We tackle the domain adaptation problem on two levels: 1) the image-level considers the differences of image color and style; 2) the feature-level addresses the spatial inconsistency between two domains. The two components are implemented as domain classifiers with adversarial training. We evaluate our new approach using two gland segmentation datasets with H&E and DAB-H stains respectively. The extensive experiments and ablation study demonstrate the effectiveness of our approach on the domain adaptive segmentation task. We show that the proposed approach performs favorably against other state-of-the-art methods.
Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images
Division of Computing and Data Science, Ruđer Bošković Institute（鲁德·波斯科维奇研究所计算与数据科学部）；Sitnik Ventures UG（Sitnik Ventures公司）；Department of Pathology and Cytology, Dubrava University Hospital（杜布拉瓦大学医院病理科与细胞学部）；Clinical Division for Pathology and Cytology, Clinical Hospital Center Zagreb（扎格雷布临床医院中心病理科与细胞学部）；Division of Molecular Medicine, Ruđer Bošković Institute（鲁德·波斯科维奇研究所分子医学部）
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…
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 …
UNICORN: A Deep Learning Model for Integrating Multi-Stain Data in Histopathology
Helmholtz Munich - German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心）；School of Computation and Information Technology, Technical University of Munich（慕尼黑工业大学计算与信息技术学院）；Department of Cardiology, German Heart Centre Munich, TUM University Hospital（慕尼黑工业大学医院德国心脏中心心脏病学系）；DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance（德国心血管研究中心慕尼黑心脏联盟合作站点）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；MLL Munich Leukemia Laboratory（MLL慕尼黑白血病实验室）
DualProtoSeg: Simple and Efficient Design with Text- and Image-Guided Prototype Learning for Weakly Supervised Histopathology Image Segmentation
University of Houston（德克萨斯大学休斯顿分校）；Ho Chi Minh City University of Technology（胡志明市技术大学）；University of Information Technology（信息技术大学）；University of Technology Sydney（悉尼技术大学）；Vin University（文大学）；Department of Computer Science, Emory University（埃默里大学计算机科学系）；College of Medical Science and Technology, Taipei Medical University（台北医学院医学科学与技术学院）；Montefiore Medical Center, Albert Einstein College of Medicine（蒙特福医院，阿尔伯特·爱因斯坦医学院）；School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）
We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We use the resulting synthetic images to build Convolutional Neural Networks (CNN) for automatic detection of mitotic figures, a strong prognostic biomarker used in routine breast cancer diagnosis and grading. We propose several scenarios, in which CNN trained with synthetically generated histopathology images perform on par with or even better than the same baseline model trained with real images. We discuss the potentia…
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…
Extracting rich phenotype information, such as cell density and arrangement, from whole slide histology images (WSIs), requires analysis of large field of view, i.e more contexual information. This can be achieved through analyzing the digital slides at lower resolution. A potential drawback is missing out on details present at a higher resolution. To jointly leverage complementary information from multiple resolutions, we present a novel transformer based Pyramidal Context-Detail Network (CD-Net). CD-Net exploits the WSI pyramidal structure through co-training of proposed Context and Detail M…
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…
University of Strasbourg（斯特拉斯堡大学）；CNRS (UMR 7357)（法国国家科学研究中心（UMR 7357））；University of Kragujevac（克拉古耶瓦茨大学）；Hannover Medical School（汉诺威医学院）
Virtual stain transfer is a promising area of research in Computational Pathology, which has a great potential to alleviate important limitations when applying deeplearningbased solutions such as lack of annotations and sensitivity to a domain shift. However, in the literature, the majority of virtual staining approaches are trained for a specific staining or stain combination, and their extension to unseen stainings requires the acquisition of additional data and training. In this paper, we propose HistoStarGAN, a unified framework that performs stain transfer between multiple stainings, stai…
University of Toronto（多伦多大学）；University of Alberta（阿尔伯塔大学）
Computational histopathology image diagnosis becomes increasingly popular and important, where images are segmented or classified for disease diagnosis by computers. While pathologists do not struggle with color variations in slides, computational solutions usually suffer from this critical issue. To address the issue of color variations in histopathology images, this study proposes two stain style transfer models, SSIM-GAN and DSCSI-GAN, based on the generative adversarial networks. By cooperating structural preservation metrics and feedback of an auxiliary diagnosis net in learning, medical-…
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…
Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham（阿姆里塔·维什瓦·维迪亚皮塔姆大学无线网络与应用中心）；Valeo（法雷奥）；Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham（阿姆里塔·维什瓦·维迪亚皮塔姆大学计算机科学与工程系）；School of Computing and Electrical Engineering, IIT Mandi（印度理工学院曼迪分校计算与电气工程学院）
Computer assisted diagnosis in digital pathology is becoming ubiquitous as it can provide more efficient and objective healthcare diagnostics. Recent advances have shown that the convolutional Neural Network (CNN) architectures, a well-established deep learning paradigm, can be used to design a Computer Aided Diagnostic (CAD) System for breast cancer detection. However, the challenges due to stain variability and the effect of stain normalization with such deep learning frameworks are yet to be well explored. Moreover, performance analysis with arguably more efficient network models, which may…
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, …
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…
Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation
University of Science, VNU-HCM（越南胡志明市科学大学）；Ho Chi Minh University of Technology（胡志明技术大学）；Vietnam National University（越南国家大学）；Carnegie Mellon University（卡内基梅隆大学）；Northwestern University（西北大学）；College of Engineering & Computer Science, VinUniversity（Vin大学工程与计算机科学学院）；VinUni-Illinois Smart Health Center, VinUniversity（Vin大学伊利诺伊智能健康中心）
In recent years, histopathology images have been increasingly used as a diagnostic tool in the medical field. The process of accurately diagnosing a biopsy sample requires significant expertise in the field, and as such can be time-consuming and is prone to uncertainty and error. With the advent of digital pathology, using image recognition systems to highlight problem areas or locate similar images can aid pathologists in making quick and accurate diagnoses. In this paper, we specifically consider the encoded local projections (ELP) algorithm, which has previously shown some success as a tool…
Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；National innovation center for advanced medical devices（国家高性能医疗器械创新中心）
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）
FedHisto-PAST: Parameter-Efficient Stain-Aware Federated Learning for Cross-Site Lung Histopathology Classification
International Islamic University Chittagong (IIUC)（吉大港国际伊斯兰大学）；American International University-Bangladesh (AIUB)（美国国际大学孟加拉分校）；King Saud University（沙特国王大学）；Charles Sturt University（查尔斯特大学）
In this paper, we develop a complete pipeline for stain normalization, segmentation, and classification of nuclei in hematoxylin and eosin (H&E) stained breast cancer histopathology images. In the first step, we use a CNN-based stain transfer technique to normalize the staining characteristics of (H&E) images. We then train a neural network to segment images of nuclei from the H&E images. Finally, we train an Information Maximizing Generative Adversarial Network (InfoGAN) to learn visual representations of different types of nuclei and classify them in an entirely unsupervised manner. The resu…
The analysis of the tumor environment on digital histopathology slides is becoming key for the understanding of the immune response against cancer, supporting the development of novel immuno-therapies. We introduce here a novel deep learning solution to the related problem of tumor epithelium segmentation. While most existing deep learning segmentation approaches are trained on time-consuming and costly manual annotation on single stain domain (PD-L1), we leverage here semi-automatically labeled images from a second stain domain (Cytokeratin-CK). We introduce an end-to-end trainable network th…
University of Strasbourg（斯特拉斯堡大学）；University of Belgrade（贝尔格莱德大学）；University of Kragujevac（克拉古耶瓦茨大学）
Domain shift in digital histopathology can occur when different stains or scanners are used, during stain translation, etc. A deep neural network trained on source data may not generalise well to data that has undergone some domain shift. An important step towards being robust to domain shift is the ability to detect and measure it. This article demonstrates that the PixelCNN and domain shift metric can be used to detect and quantify domain shift in digital histopathology, and they demonstrate a strong correlation with generalisation performance. These findings pave the way for a mechanism to …
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…
Generative Adversarial Networks for Stain Normalisation in Histopathology
University of Leeds（利兹大学）；Leeds Cancer Centre（利兹癌症中心）；St James’s University Hospital（圣詹姆斯大学医院）；Leeds Institute of Medical Research at St James’s（圣詹姆斯利兹医学研究所）；School of Medicine（医学院）；School of Computing（计算机学院）；Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)（生物医学计算成像与模拟技术中心（CISTIB））
Vision-Language Models as Zero-Annotation Oracles in Histopathology
Imperial College London（帝国理工学院）；Leiden University Medical Center（莱顿大学医学中心）；KU Leuven（鲁汶大学）；University Hospitals Leuven（鲁汶大学医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Friedrich-Alexander University Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）
Histopathology Image Normalization via Latent Manifold Compaction
Knight Cancer Institute, Oregon Health and Science University（骑士癌症研究所，俄勒冈健康与科学大学）；Brenden-Colson Center for Pancreatic Care, Oregon Health and Science University（布雷登-科尔森胰腺护理中心，俄勒冈健康与科学大学）
Radboud University Medical Center（拉德堡德大学医学中心）；Linköping University（林雪平大学）
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with images from one lab often underperform on unseen images from the other lab. Several techniques have been proposed to reduce the generalization error, mainly grouped into two categories: stain color augmentation and stain color normalization. The former simulates a wide variety of realistic stain variations during training, producing stain-invarian…
Rutgers University（罗格斯大学）；Rutgers Cancer Institute of New Jersey（新泽西罗格斯癌症研究所）
Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis. Usually, histopathology tissue slides from different institutions show heterogeneous appearances because of different tissue preparation and staining procedures, thus the predictable model learned from one domain may not be applicable to a new domain directly. Here we propose to adopt unsupervised domain adaptation to transfer the discriminative knowledge obtained from the source domain to the target domain without requiring labeling of images at the target…
HAPS: Rethinking Image Similarity for Virtual Staining
Skolkovo Institute of Science and Technology（斯克洛洛沃科学与技术研究所）；University of Sharjah（沙迦大学）；National Medical Research Radiological Centre of the Ministry of Health of the Russian Federation（俄罗斯联邦卫生部国家医学研究放射中心）
Shanghai Jiao Tong University（上海交通大学）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；University of New South Wales（新南威尔士大学）；BirenTech Research（壁仞科技研究院）
Stain variations often decrease the generalization ability of deep learning based approaches in digital histopathology analysis. Two separate proposals, namely stain normalization (SN) and stain augmentation (SA), have been spotlighted to reduce the generalization error, where the former alleviates the stain shift across different medical centers using template image and the latter enriches the accessible stain styles by the simulation of more stain variations. However, their applications are bounded by the selection of template images and the construction of unrealistic styles. To address the…
Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters. The parameters can vary between pathology labs and within the same lab over time, resulting in significant variability of the tissue appearance that hampers the generalization of automatic image analysis methods. Typically, this is addressed with ad-hoc approaches such as staining normalization that aim to reduce the appearance variability. In this paper, we propose a systematic solution based on domain-adversarial neural networks. We hypothesize that removing the domain information fro…
Histopathology whole slide images (WSIs) can reveal significant inter-hospital variability such as illumination, color or optical artifacts. These variations, caused by the use of different scanning protocols across medical centers (staining, scanner), can strongly harm algorithms generalization on unseen protocols. This motivates development of new methods to limit such drop of performances. In this paper, to enhance robustness on unseen target protocols, we propose a new test-time data augmentation based on multi domain image-to-image translation. It allows to project images from unseen prot…
Institute of Information Systems, HES-SO (University of Applied Sciences and Arts Western Switzerland)（瑞士西部应用科学与艺术大学 信息系统研究所）；Department of Computer Science, University of Geneva（日内瓦大学 计算机科学系）；Department of Neurosciences, University of Padua（帕多瓦大学 神经科学系）；Inselspital, Bern Hospital（伯尔尼大学医院）；Medical Faculty, University of Geneva（日内瓦大学 医学院）
Computational pathology is a domain that aims to develop algorithms to automatically analyze large digitized histopathology images, called whole slide images (WSI). WSIs are produced scanning thin tissue samples that are stained to make specific structures visible. They show stain colour heterogeneity due to different preparation and scanning settings applied across medical centers. Stain colour heterogeneity is a problem to train convolutional neural networks (CNN), the state-of-the-art algorithms for most computational pathology tasks, since CNNs usually underperform when tested on images in…
Digital Environment Research Institute（数字环境研究所）；Queen Mary University of London（伦敦玛丽女王大学）；William Harvey Research Institute（威廉·哈维研究所）；The Hong Kong Polytechnic University（香港理工大学）
Stony Brook University（石溪大学）；Dougherty Valley High School（多尔蒂谷高中）；Oak Ridge National Laboratory（橡树岭国家实验室）；Stony Brook Hospital（石溪医院）
Hematoxylin and Eosin stained histopathology image analysis is essential for the diagnosis and study of complicated diseases such as cancer. Existing state-of-the-art approaches demand extensive amount of supervised training data from trained pathologists. In this work we synthesize in an unsupervised manner, large histopathology image datasets, suitable for supervised training tasks. We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images throu…
University of Utah（犹他大学）；Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；Huntsman Cancer Institute, University of Utah（犹他大学亨茨曼癌症研究所）
We propose a novel semi-supervised learning approach for classification of histopathology images. We employ strong supervision with patch-level annotations combined with a novel co-training loss to create a semi-supervised learning framework. Co-training relies on multiple conditionally independent and sufficient views of the data. We separate the hematoxylin and eosin channels in pathology images using color deconvolution to create two views of each slide that can partially fulfill these requirements. Two separate CNNs are used to embed the two views into a joint feature space. We use a contr…
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…
Unsupervised and unpaired domain translation using generative adversarial neural networks, and more precisely CycleGAN, is state of the art for the stain translation of histopathology images. It often, however, suffers from the presence of cycle-consistent but non structure-preserving errors. We propose an alternative approach to the set of methods which, relying on segmentation consistency, enable the preservation of pathology structures. Focusing on immunohistochemistry (IHC) and multiplexed immunofluorescence (mIF), we introduce a simple yet effective guidance scheme as a loss function that…
ICube, University of Strasbourg, CNRS (UMR 7357)（ICube研究所，斯特拉斯堡大学，CNRS（UMR 7357））；Institute of Pathology, Hannover Medical School（病理研究所，汉诺威医学院）
Semantic segmentation under domain shift remains a fundamental challenge in computer vision, particularly when labelled training data is scarce. This challenge is particularly exemplified in histopathology image analysis, where the same tissue structures must be segmented across images captured under different imaging conditions (stains), each representing a distinct visual domain. Traditional deep learning methods like UNet require extensive labels, which is both costly and time-consuming, particularly when dealing with multiple domains (or stains). To mitigate this, various unsupervised doma…
Performance of deep learning algorithms decreases drastically if the data distributions of the training and testing sets are different. Due to variations in staining protocols, reagent brands, and habits of technicians, color variation in digital histopathology images is quite common. Color variation causes problems for the deployment of deep learning-based solutions for automatic diagnosis system in histopathology. Previously proposed color normalization methods consider a small patch as a reference for normalization, which creates artifacts on out-of-distribution source images. These methods…
DiffusionQC: Artifact Detection in Histopathology via Diffusion Model
Johns Hopkins University Dept. of Biomedical Engineering（约翰霍普金斯大学生物医学工程系）；Merck & Co., Inc. Biometrics Research（默克公司生物度量研究）；Merck & Co., Inc. Translational Medicine（默克公司转化医学）
Linköping University（林雪平大学）；Sectra AB（斯凯特拉公司）；Center for Medical Image Science and Visualization（医学影像科学与可视化中心）
Domain shift is a significant problem in histopathology. There can be large differences in data characteristics of whole-slide images between medical centers and scanners, making generalization of deep learning to unseen data difficult. To gain a better understanding of the problem, we present a study on convolutional neural networks trained for tumor classification of H&E stained whole-slide images. We analyze how augmentation and normalization strategies affect performance and learned representations, and what features a trained model respond to. Most centrally, we present a novel measure fo…
Radboud University Medical Center（拉德堡德大学医学中心）；Laboratorium Pathologie Oost Nederland（东荷兰病理学实验室）
The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image. In this paper, we propose a system for CRC tissue classification based on convolutional networks (ConvNets). We investigate the importance of stain normalization in tissue classification of CRC tissue samples in H&E-stained images. Furthermore, we report the performance of ConvNets on a cohort of rectal cancer samples and on an independent publicly …
