Histopathology refers to the examination by a pathologist of biopsy samples. Histopathology images are captured by a microscope to locate, examine, and classify many diseases, such as different cancer types. They provide a detailed view of different types of diseases and their tissue status. These images are an essential resource with which to define biological compositions or analyze cell and tissue structures. This imaging modality is very important for diagnostic applications. The analysis of histopathology images is a prolific and relevant research area supporting disease diagnosis. In this paper, the challenges of histopathology image analysis are evaluated. An extensive review of conventional and deep learning techniques which have been applied in histological image analyses is presented. This review summarizes many current datasets and highlights important challenges and constraints with recent deep learning techniques, alongside possible future research avenues. Despite the progress made in this research area so far, it is still a significant area of open research because of the variety of imaging techniques and disease-specific characteristics.
In practice, histopathological diagnosis of tumor malignancy often requires a human expert to scan through histopathological images at multiple magnification levels, after which a final diagnosis can be accurately determined. However, previous research on such classification tasks using convolutional neural networks primarily determine a diagnosis for a single magnification level. In this paper, we propose a case-based approach using deep residual neural networks for histopathological malignancy diagnosis, where a case is defined as a sequence of images from the patient at all available levels…
Süleyman Demirel University（苏莱曼·德米雷尔大学）；Burdur Mehmet Akif Ersoy University（布尔杜尔穆罕默德·阿基夫·埃尔索伊大学）；Karunya Institute of Technology & Sciences（卡鲁尼亚技术与科学学院）
Artificial intelligence holds great promise in medical imaging, especially histopathological imaging. However, artificial intelligence algorithms cannot fully explain the thought processes during decision-making. This situation has brought the problem of explainability, i.e., the black box problem, of artificial intelligence applications to the agenda: an algorithm simply responds without stating the reasons for the given images. To overcome the problem and improve the explainability, explainable artificial intelligence (XAI) has come to the fore, and piqued the interest of many researchers. A…
Northeastern University（东北大学）；China Medical University（中国医科大学）；University of Luebeck（吕贝克大学）
The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. In this paper, based on the study of computer aided diagnosis system, graph based features are applied to gastric cancer histopathology microscopic image analysis, and a classifier is used to classify gastric cancer cells from benign cells. Firstly, image segmentation is performed, and after finding the region, cell nuclei are extracted using the k-means method, the minimum spanning tree (MST) is drawn, and graph …
Transfer learning allows us to exploit knowledge gained from one task to assist in solving another but relevant task. In modern computer vision research, the question is which architecture performs better for a given dataset. In this paper, we compare the performance of 14 pre-trained ImageNet models on the histopathologic cancer detection dataset, where each model has been configured as a naive model, feature extractor model, or fine-tuned model. Densenet161 has been shown to have high precision whilst Resnet101 has a high recall. A high precision model is suitable to be used when follow-up e…
University of Virginia（弗吉尼亚大学）；Queen Mary University of London（伦敦玛丽女王大学）；University of Zambia（赞比亚大学）；Washington University（华盛顿大学）；Aga Khan University（阿迦汗大学）
Deep convolutional neural networks(CNNs) have been successful for a wide range of computer vision tasks, including image classification. A specific area of the application lies in digital pathology for pattern recognition in the tissue-based diagnosis of gastrointestinal(GI) diseases. This domain can utilize CNNs to translate histopathological images into precise diagnostics. This is challenging since these complex biopsies are heterogeneous and require multiple levels of assessment. This is mainly due to structural similarities in different parts of the GI tract and shared features among diff…
Vietnam Aviation Academy（越南航空学院）；Ho Chi Minh City Open University（胡志明市开放大学）；University of Information Technology, VNU-HCM（胡志明市国家大学信息技术大学）
According to some medical imaging techniques, breast histopathology images called Hematoxylin and Eosin are considered as the gold standard for cancer diagnoses. Based on the idea of dividing the pathologic image (WSI) into multiple patches, we used the window [512,512] sliding from left to right and sliding from top to bottom, each sliding step overlapping by 50% to augmented data on a dataset of 400 images which were gathered from the ICIAR 2018 Grand Challenge. Then use the EffficientNet model to classify and identify the histopathological images of breast cancer into 4 types: Normal, Benig…
Celiac Disease (CD) is a chronic autoimmune disease that affects the small intestine in genetically predisposed children and adults. Gluten exposure triggers an inflammatory cascade which leads to compromised intestinal barrier function. If this enteropathy is unrecognized, this can lead to anemia, decreased bone density, and, in longstanding cases, intestinal cancer. The prevalence of the disorder is 1% in the United States. An intestinal (duodenal) biopsy is considered the "gold standard" for diagnosis. The mild CD might go unnoticed due to non-specific clinical symptoms or mild histologic f…
Universidade Estadual de Ponta Grossa（蓬塔格罗萨州立大学）；Pontificia Universidade Católica do Paraná（巴拉那天主教大学）；Universidade Federal do Paraná（巴拉那联邦大学）；École de Technologie Supérieure（高等技术学院）
Biopsies are the gold standard for breast cancer diagnosis. This task can be improved by the use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and reducing the inter and intra-observer variability. The advances in computing have brought this type of system closer to reality. However, datasets of Histopathological Images (HI) from biopsies are quite small and unbalanced what makes difficult to use modern machine learning techniques such as deep learning. In this paper we propose a compact architecture based on texture filters that has fewer parameters than traditiona…
DiagSet: a dataset for prostate cancer histopathological image classification
Diagnostyka Consilio Sp. z o.o.（诊断康西利奥有限公司）；AGH University of Science and Technology（AGH科技大学）；Andrzej Frycz Modrzewski Krakow University（安德烈·弗赖奇·莫德热夫斯基克拉科夫大学）
Xi'an Jiao tong University（西安交通大学）；School of Electronic and Information Engineering, Xi'an Jiao tong University（西安交通大学电子与信息工程学院）；the First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）；Xidian University（西安电子科技大学）
Pathological is crucial to cancer diagnosis. Usually, Pathologists draw their conclusion based on observed cell and tissue structure on histology slides. Rapid development in machine learning, especially deep learning have established robust and accurate classifiers. They are being used to analyze histopathological slides and assist pathologists in diagnosis. Most machine learning systems rely heavily on annotated data sets to gain experiences and knowledge to correctly and accurately perform various tasks such as classification and segmentation. This work investigates different granularity of…
Lung and Colon cancer are one of the leading causes of mortality and morbidity in adults. Histopathological diagnosis is one of the key components to discern cancer type. The aim of the present research is to propose a computer aided diagnosis system for diagnosing squamous cell carcinomas and adenocarcinomas of lung as well as adenocarcinomas of colon using convolutional neural networks by evaluating the digital pathology images for these cancers. Hereby, rendering artificial intelligence as useful technology in the near future. A total of 2500 digital images were acquired from LC25000 datase…
University of Ljubljana（卢布尔雅那大学）；XLAB d.o.o.（XLAB公司）
Anomaly detection in visual data refers to the problem of differentiating abnormal appearances from normal cases. Supervised approaches have been successfully applied to different domains, but require an abundance of labeled data. Due to the nature of how anomalies occur and their underlying generating processes, it is hard to characterize and label them. Recent advances in deep generative-based models have sparked interest in applying such methods for unsupervised anomaly detection and have shown promising results in medical and industrial inspection domains. In this work we evaluate a crucia…
Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a subtype of cancer that constitutes more than 80% of all cancer cases. Squamous cell carcinoma and adenocarcinoma are two major subtypes of carcinoma, diagnosed by microscopic study of biopsy slides. However, manual microscopic evaluation is a subjective and time-consuming process. Many researchers have reported methods to automate carcinoma detection and classification. The increasing use of artificial intelligence (AI) in the automation of carcinoma diagnosis also reveals a significant rise in the use of …
Automated histopathological image analysis offers exciting opportunities for the early diagnosis of several medical conditions including cancer. There are however stiff practical challenges: 1.) discriminative features from such images for separating diseased vs. healthy classes are not readily apparent, and 2.) distinct classes, e.g. healthy vs. stages of disease continue to share several geometric features. We propose a novel Analysis-synthesis model Learning with Shared Features algorithm (ALSF) for classifying such images more effectively. In ALSF, a joint analysis and synthesis learning m…
Kepler University Hospital（开普勒大学医院）；Johannes Kepler University Linz（林茨约翰内斯·开普勒大学）；Medical University of Vienna（维也纳医科大学）
Diagnosing basal cell carcinomas (BCC), one of the most common cutaneous malignancies in humans, is a task regularly performed by pathologists and dermato-pathologists. Improving histological diagnosis by providing diagnosis suggestions, i.e. computer-assisted diagnoses is actively researched to improve safety, quality and efficiency. Increasingly, machine learning methods are applied due to their superior performance. However, typical images obtained by scanning histological sections often have a resolution that is prohibitive for processing with current state-of-the-art neural networks. Furt…
Northeastern University（东北大学）；Cancer Hospital, China Medical University（中国医科大学肿瘤医院）；Liaoning Cancer Hospital and Institute（辽宁省肿瘤医院暨研究所）；University of New South Wales（新南威尔士大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Shengjing Hospital, China Medical University（中国医科大学附属盛京医院）；Liaoning Shihua University（辽宁石油化工大学）；University of Luebeck（吕贝克大学）
Background and purpose: Colorectal cancer has become the third most common cancer worldwide, accounting for approximately 10% of cancer patients. Early detection of the disease is important for the treatment of colorectal cancer patients. Histopathological examination is the gold standard for screening colorectal cancer. However, the current lack of histopathological image datasets of colorectal cancer, especially enteroscope biopsies, hinders the accurate evaluation of computer-aided diagnosis techniques. Methods: A new publicly available Enteroscope Biopsy Histopathological H&E Image Dataset…
Northeastern University（东北大学）；Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute（中国医科大学附属肿瘤医院、辽宁省肿瘤医院暨研究所）
Background and Purpose: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segmentation of rectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentati…
University of Washington（华盛顿大学）；University of Vermont College of Medicine（佛蒙特大学医学院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Training end-to-end networks for classifying gigapixel size histopathological images is computationally intractable. Most approaches are patch-based and first learn local representations (patch-wise) before combining these local representations to produce image-level decisions. However, dividing large tissue structures into patches limits the context available to these networks, which may reduce their ability to learn representations from clinically relevant structures. In this paper, we introduce a novel attention-based network, the Holistic ATtention Network (HATNet) to classify breast biops…
Histology method is vital in the diagnosis and prognosis of cancers and many other diseases. For the analysis of histopathological images, we need to detect and segment all gland structures. These images are very challenging, and the task of segmentation is even challenging for specialists. Segmentation of glands determines the grade of cancer such as colon, breast, and prostate. Given that deep neural networks have achieved high performance in medical images, we propose a method based on the LinkNet network for gland segmentation. We found the effects of using different loss functions. By usi…
Xi’an Jiaotong University（西安交通大学）；Pennsylvania State University（宾夕法尼亚州立大学）；the First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）
Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. Tumor region detection, subtype and grade classification are the fundamental diagnostic indicators for renal cell carcinoma (RCC) in whole-slide images (WSIs). However, pathological diagnosis is subjective, differences in observation and diagnosis between pathologists is common in hospitals with inadequate diagnostic capacity. The main challenge for developing deep learning based RCC diagnostic system is the la…
University of Saskatchewan（萨斯喀彻温大学）；University of Tulane（杜兰大学）
Breast cancer is one of the leading causes of death across the world in women. Early diagnosis of this type of cancer is critical for treatment and patient care. Computer-aided detection (CAD) systems using convolutional neural networks (CNN) could assist in the classification of abnormalities. In this study, we proposed an ensemble deep learning-based approach for automatic binary classification of breast histology images. The proposed ensemble model adapts three pre-trained CNNs, namely VGG19, MobileNet, and DenseNet. The ensemble model is used for the feature representation and extraction s…
Umm Al-Qura University（乌姆·库拉大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；University of Tabuk（塔布克大学）；King Faisal Specialist Hospital and Research Center（费萨尔国王专科医院和研究中心）；University of Nevada（内华达大学）
Breast cancer is the most common cancer in the world and the second most common type of cancer that causes death in women. The timely and accurate diagnosis of breast cancer using histopathological images is crucial for patient care and treatment. Pathologists can make more accurate diagnoses with the help of a novel approach based on image processing. This approach is an ensemble model of two types of pre-trained vision transformer models, namely, Vision Transformer and Data-Efficient Image Transformer. The proposed ensemble model classifies breast cancer histopathology images into eight clas…
Accurate segmentation of medical images is essential for diagnosis and treatment of diseases. These problems are solved by highly complex models, such as deep networks (DN), requiring a large amount of labeled data for training. Thereby, many DNs possess task- or imaging modality specific architectures with a decision-making process that is often hard to explain and interpret. Here, we propose a framework that embeds existing DNs into a low-dimensional subspace induced by the learnable explicit feature map (LEFM) layer. Compared to the existing DN, the framework adds one hyperparameter and onl…
Mansoura University（曼苏拉大学）；Higher Institute of Engineering and Technology（高等工程技术学院）
Breast cancer is one of the most serious types of cancer that can occur in women. The automatic diagnosis of breast cancer by analyzing histological images (HIs) is important for patients and their prognosis. The classification of HIs provides clinicians with an accurate understanding of diseases and allows them to treat patients more efficiently. Deep learning (DL) approaches have been successfully employed in a variety of fields, particularly medical imaging, due to their capacity to extract features automatically. This study aims to classify different types of breast cancer using HIs. In th…
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images
Pakistan Institute of Engineering & Applied Sciences（巴基斯坦工程与应用科学学院）；Air University（空军大学）；Universiti Teknologi PETRONAS（马来西亚国油科技大学）；Korea National University of Transportation（韩国交通大学）
Northeastern University（东北大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Cancer Hospital of China Medical University（中国医科大学附属肿瘤医院）；Chengdu University of Information Technology（成都信息工程大学）
Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems int…
Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach
Tyris Tech S.L.（泰瑞斯科技公司）；Universitat Politècnica de València（瓦伦西亚理工大学）；ValgrAI – Valencian Graduate School and Research Network for Artificial Intelligence（瓦伦西亚人工智能研究生院与研究网络）
Qatar University（卡塔尔大学）；Izmir University of Economics（伊兹密尔经济大学）；Tampere University of Technology（坦佩雷理工大学）
Computer-aided diagnosis (CAD) based on histopathological imaging has progressed rapidly in recent years with the rise of machine learning based methodologies. Traditional approaches consist of training a classification model using features extracted from the images, based on textures or morphological properties. Recently, deep-learning based methods have been applied directly to the raw (unprocessed) data. However, their usability is impacted by the paucity of annotated data in the biomedical sector. In order to leverage the learning capabilities of deep Convolutional Neural Nets (CNNs) withi…
Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive cases were diagnosed in the US. To put these figures in perspective, 64% of these cases are diagnosed early in the disease's cycle, giving patients a 99% chance of survival. Artificial intelligence and machine learning have been used effectively in detection and treatment of severa…
DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology
University of Padova, Italy（意大利帕多瓦大学）；Unit of Surgical Pathology and Cytopathology, Ospedale dell’Angelo, Italy（意大利奥斯佩达莱-安吉洛外科病理学与细胞病理学单位）；Pathological Anatomy Unit, Padova University-Hospital, Italy（意大利帕多瓦大学医院病理学解剖单位）；Department of Medicine, University of Padova, Italy（意大利帕多瓦大学医学系）；Department of Integrated Diagnostics, Azienda Ospedale-Università Padova, Italy（意大利帕多瓦医院-大学整合诊断系）
AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions
University Center Salhi Ahmed（萨利赫·艾哈迈德大学中心）；University of Dubai（迪拜大学）；University of Medea（麦迪亚大学）；University of Jijel（吉杰勒大学）；University of Yahia Fares Medea（叶海亚·法雷斯·麦迪亚大学）；Mohamed Khider University（穆罕默德·希德尔大学）
Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities. In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The model incorporates multiple CNNs including Outer, Middle, and Inner. The first t…
Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Zhongnan Hospital of Wuhan University（武汉大学中南医院）；Fuzhou University Affiliated Provincial Hospital（福州大学附属省立医院）；Liuzhou People's Hospital Affiliated to Guangxi Medical University（广西医科大学附属柳州市人民医院）；The Second Affiliated Hospital of Southern University of Science and Technology（南方科技大学第二附属医院）；Shenzhen Zhengjingda Instrument Co., Ltd.（深圳正景达仪器有限公司）；Chongqing University Affiliated Three Gorges Hospital（重庆大学附属三峡医院）；Shenzhen Shengqiang Technology Co., Ltd.（深圳盛强科技有限公司）；Longgang Central Hospital of Shenzhen（深圳市龙岗中心医院）；Ningbo Clinical Pathology Diagnosis Center（宁波市临床病理诊断中心）
TU Berlin（柏林工业大学）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Charité University Hospital（夏里特大学医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Korea University（高丽大学）；Max-Planck-Institute for Informatics（马克斯·普朗克信息学研究所）；Singapore University of Technology and Design（新加坡科技设计大学）
Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better understanding beyond standard quantitative performance evaluation. Recently, explanation methods have emerged, which are so far still rarely used in medicine. This work shows their application to generate heatmaps that allow to resolve common challenges encountered in deep learning-based digital histopathology analyses. These challenges comprise biases typically inherent to histopathology data. We study binary classifica…
Northeastern University（东北大学）；University of the Chinese Academy of Sciences（中国科学院大学）；China Medical University（中国医科大学）
The deep neural network is a research hotspot for histopathological image analysis, which can improve the efficiency and accuracy of diagnosis for pathologists or be used for disease screening. The whole slide pathological image can reach one gigapixel and contains abundant tissue feature information, which needs to be divided into a lot of patches in the training and inference stages. This will lead to a long convergence time and large memory consumption. Furthermore, well-annotated data sets are also in short supply in the field of digital pathology. Inspired by the pathologist's clinical di…
Faculty of Sciences（理学院）；Mohammed V University（穆罕默德五世大学）；École Normale Supérieure（高等师范学院）
Cervical cancer will cause 460 000 deaths per year by 2040, approximately 90% are Sub-Saharan African women. A constantly increasing incidence in Africa making cervical cancer a priority by the World Health Organization (WHO) in terms of screening, diagnosis, and treatment. Conventionally, cancer diagnosis relies primarily on histopathological assessment, a deeply error-prone procedure requiring intelligent computer-aided systems as low-cost patient safety mechanisms but lack of labeled data in digital pathology limits their applicability. In this study, few cervical tissue digital slides from…
Emory University（埃默里大学）；Stony Brook University（石溪大学）；Georgia State University（佐治亚州立大学）
Liver steatosis is known as the abnormal accumulation of lipids within cells. An accurate quantification of steatosis area within the liver histopathological microscopy images plays an important role in liver disease diagnosis and trans-plantation assessment. Such a quantification analysis often requires a precise steatosis segmentation that is challenging due to abundant presence of highly overlapped steatosis droplets. In this paper, a deep learning model Mask-RCNN is used to segment the steatosis droplets in clumps. Extended from Faster R-CNN, Mask-RCNN can predict object masks in addition …
CBIDR: A novel method for information retrieval combining image and data by means of TOPSIS applied to medical diagnosis
Labcin - Nature-inspired computing Lab. - Federal University of Espírito Santo - Vitória, ES, Brazil（自然启发计算实验室 - 芬斯大学 - 维蒂利亚）；Graduate Program in Computer Science, PPGI, UFES - Federal University of Espírito Santo - Vitória, ES, Brazil（计算机科学研究生项目，PPGI，UFES - 芬斯大学 - 维蒂利亚）
Equivariant Imaging Biomarkers for Robust Unsupervised Segmentation of Histopathology
Department of Biomedical Engineering, Yale University（生物医学工程系，耶鲁大学）；Department of Radiology & Biomedical Imaging, Yale University（放射科与生物医学成像系，耶鲁大学）；Department of Urology, Yale University（泌尿外科系，耶鲁大学）；Medical Scientist Training Program, Yale University（医学科学家培训计划，耶鲁大学）
Jagannath University（贾甘纳特大学）；RMIT University（皇家墨尔本理工大学）；The University of Queensland（昆士兰大学）
Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action. Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients…
Histopathological images provide rich information for disease diagnosis. Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement. Most previous work on whole slide image analysis has focused on classification or segmentation of small pre-selected regions-of-interest, which requires fine-grained annotation and is non-trivial to extend for large-scale whole slide analysis. In this paper,…
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 the last decade, researchers working in the domain of computer vision and Artificial Intelligence (AI) have beefed up their efforts to come up with the automated framework that not only detects but also identifies stage of breast cancer. The reason for this surge in research activities in this direction are mainly due to advent of robust AI algorithms (deep learning), availability of hardware that can train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers…
A Polarization and Radiomics Feature Fusion Network for the Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma
University College London（伦敦大学学院）；Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Fujian Medical University Cancer Hospital（福建医科大学肿瘤医院）；Fujian Cancer Hospital（福建省肿瘤医院）
