Convolutional Neural Networks can be designed with different levels of complexity depending upon the task at hand. This paper analyzes the effect of dimensional changes to the CNN architecture on its performance on the task of Histopathological Cancer Classification. The research starts with a baseline 10-layer CNN model with (3 X 3) convolution filters. Thereafter, the baseline architecture is scaled in multiple dimensions including width, depth, resolution and a combination of all of these. Width scaling involves inculcating greater number of neurons per CNN layer, whereas depth scaling involves deepening the hierarchical layered structure. Resolution scaling is performed by increasing the dimensions of the input image, and compound scaling involves a hybrid combination of width, depth and resolution scaling. The results indicate that histopathological cancer scans are very complex in nature and hence require high resolution images fed to a large hierarchy of Convolution, MaxPooling, Dropout and Batch Normalization layers to extract all the intricacies and perform perfect classification. Since compound scaling the baseline model ensures that all the three dimensions: width, depth and resolution are scaled, the best performance is obtained with compound scaling. This research shows that better performance of CNN models is achieved by compound scaling of the baseline model for the task of Histopathological Cancer Classification.
Tianjin University（天津大学）；CSIRO Data61（澳大利亚联邦科学与工业研究组织Data61）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Shandong University（山东大学）
Ovarian cancer is one of the most serious cancers that threaten women around the world. Epithelial ovarian cancer (EOC), as the most commonly seen subtype of ovarian cancer, has rather high mortality rate and poor prognosis among various gynecological cancers. Survival analysis outcome is able to provide treatment advices to doctors. In recent years, with the development of medical imaging technology, survival prediction approaches based on pathological images have been proposed. In this study, we designed a deep framework named EOCSA which analyzes the prognosis of EOC patients based on patho…
INSA Centre Val de Loire（卢瓦尔河谷国立应用科学学院）；PRISME（PRISME实验室）
This paper addresses the problem of liver cancer segmentation in Whole Slide Image (WSI). We propose a multi-scale image processing method based on automatic end-to-end deep neural network algorithm for segmentation of cancer area. A seven-levels gaussian pyramid representation of the histopathological image was built to provide the texture information in different scales. In this work, several neural architectures were compared using the original image level for the training procedure. The proposed method is based on U-Net applied to seven levels of various resolutions (pyramidal subsumpling)…
Department of Electrical and Electronics Engineering, Shiraz University of Technology（设拉子理工大学电气与电子工程系）；Department of Pathology, School of Medicine, Shiraz University of Medical Sciences（设拉子医科大学医学院病理学系）
Early detection and prognosis of breast cancer are feasible by utilizing histopathological grading of biopsy specimens. This research is focused on detection and grading of nuclear pleomorphism in histopathological images of breast cancer. The proposed method consists of three internal steps. First, unmixing colors of H&E is used in the preprocessing step. Second, nuclei boundaries are extracted incorporating the center of cancerous nuclei which are detected by applying morphological operations and Difference of Gaussian filter on the preprocessed image. Finally, segmented nuclei are scored to…
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）
Nagoya Institute of Technology（名古屋工业大学）；Nagoya University Hospital（名古屋大学医院）；RIKEN（理化学研究所）
We propose a new method for cancer subtype classification from histopathological images, which can automatically detect tumor-specific features in a given whole slide image (WSI). The cancer subtype should be classified by referring to a WSI, i.e., a large-sized image (typically 40,000x40,000 pixels) of an entire pathological tissue slide, which consists of cancer and non-cancer portions. One difficulty arises from the high cost associated with annotating tumor regions in WSIs. Furthermore, both global and local image features must be extracted from the WSI by changing the magnifications of th…
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）
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…
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 …
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…
PIEAS（巴基斯坦工程与应用科学学院）；Islamia College Peshawar（白沙瓦伊斯兰学院）
Empirical evaluation of breast tissue biopsies for mitotic nuclei detection is considered an important prognostic biomarker in tumor grading and cancer progression. However, automated mitotic nuclei detection poses several challenges because of the unavailability of pixel-level annotations, different morphological configurations of mitotic nuclei, their sparse representation, and close resemblance with non-mitotic nuclei. These challenges undermine the precision of the automated detection model and thus make detection difficult in a single phase. This work proposes an end-to-end detection syst…
Universitat Politècnica de València（瓦伦西亚理工大学）；University of Stavanger（斯塔万格大学）；Tyris Tech S.L.（泰里斯科技有限公司）
According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate cancer is challenging, especially when discriminating between Grade 3 (G3) and Grade 4 (G4). This paper proposes a Self-Supervised Learning (SSL) framework to classify prostate histopathological images when labeled images are scarce. In particular, a tailored Convolutional Auto Encoder (CAE) is trained to reconstruct 128x128x3 patches of prostate cancer Whole Slide Images (WSIs) as a pretext task. The downstream task of t…
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…
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…
There exists unexplained diverse variation within the predefined colon cancer stages using only features either from genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about improved in staging and treatment outcome, hence motivated by the advancement of Deep Neural Network libraries and different structures and factors within some genomic dataset, we aggregate atypical patterns in histopathological images with diverse carcinogenic expression from mRNA, miRNA and DNA Methylation as an integrative input source into an ensemble deep neura…
Techno India University, West Bengal（西孟加拉邦Techno印度大学）
In this paper we present a novel method for an automated diagnosis of breast carcinoma through multilevel iterative variational mode decomposition (VMD) and textural features encompassing Zernaike moments, fractal dimension and entropy features namely, Kapoor entropy, Renyi entropy, Yager entropy features are extracted from VMD components. The proposed method considers the histopathological image as a set of multidimensional spatially-evolving signals. ReliefF algorithm is used to select the discriminatory features and statistically most significant features are fed to squares support vector m…
Indian Institute of Information Technology, Sri City（斯里城印度信息技术学院）
Deep learning has shown a great improvement in the performance of visual tasks. Image retrieval is the task of extracting the visually similar images from a database for a query image. The feature matching is performed to rank the images. Various hand-designed features have been derived in past to represent the images. Nowadays, the power of deep learning is being utilized for automatic feature learning from data in the field of biomedical image analysis. Autoencoder and Siamese networks are two deep learning models to learn the latent space (i.e., features or embedding). Autoencoder works bas…
The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Breast cancer is one of the most common and dangerous cancers impacting women worldwide. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful DCNN model that combines the strength of the Inception…
Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer
University of Jyväskylä（于韦斯屈莱大学）；Oulu University Hospital（奥卢大学医院）；University of Oulu（奥卢大学）；Tampere University（坦佩雷大学）；University of Turku（图尔库大学）；Turku University Hospital（图尔库大学医院）；Hospital Nova of Central Finland（中芬兰新星医院）
本文提出基于卷积神经网络的结直肠癌组织病理图像 TSR 自动估算方法，评估领域特定迁移学习，最高三类分类准确率达 96.1%，与病理学家评估相关性为 0.57。
Cancer is one of the most common and fatal diseases in the world. Breast cancer affects one in every eight women and one in every eight hundred men. Hence, our prime target should be early detection of cancer because the early detection of cancer can be helpful to cure cancer effectively. Therefore, we propose a saliency detection system with the help of advanced deep learning techniques, such that the machine will be taught to emulate actions of pathologists for localization of diagnostically pertinent regions. We study identification of five diagnostic categories of breast cancer by training…
George Washington University（乔治华盛顿大学）；University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）
This paper constructs a novel intelligent medical diagnosis system, which can realize automatic communication and breast cancer pathological image recognition. This system contains two main parts, including a pre-training chatbot called M-Chatbot and an improved neural network model of EfficientNetV2-S named EfficientNetV2-SA, in which the activation function in top layers is replaced by ACON-C. Using information retrieval mechanism, M-Chatbot instructs patients to send breast pathological image to EfficientNetV2-SA network, and then the classifier trained by transfer learning will return the …
Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides
Jilin University（吉林大学）；China-Japan Union Hospital of Jilin University（吉林大学中日联谊医院）；Nanjing University of Science and Technology（南京理工大学）；Tongji University（同济大学）
Jaypee University of Information Technology（贾皮尔信息技术大学）
Colon and Lung cancer is one of the most perilous and dangerous ailments that individuals are enduring worldwide and has become a general medical problem. To lessen the risk of death, a legitimate and early finding is particularly required. In any case, it is a truly troublesome task that depends on the experience of histopathologists. If a histologist is under-prepared it may even hazard the life of a patient. As of late, deep learning has picked up energy, and it is being valued in the analysis of Medical Imaging. This paper intends to utilize and alter the current pre-trained CNN-based mode…
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…
Synthetic generation of three-dimensional cell models from histopathological images aims to enhance understanding of cell mutation, and progression of cancer, necessary for clinical assessment and optimal treatment. Classical reconstruction algorithms based on image registration of consecutive slides of stained tissues are prone to errors and often not suitable for the training of three-dimensional segmentation algorithms. We propose a novel framework to generate synthetic three-dimensional histological models based on a generator-discriminator pattern optimizing constrained features that cons…
Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València（瓦伦西亚理工大学生物工程研究与创新研究所）；Hospital Universitario y Politécnico La Fe（拉费大学理工医院）
Recently, bladder cancer has been significantly increased in terms of incidence and mortality. Currently, two subtypes are known based on tumour growth: non-muscle invasive (NMIBC) and muscle-invasive bladder cancer (MIBC). In this work, we focus on the MIBC subtype because it is of the worst prognosis and can spread to adjacent organs. We present a self-learning framework to grade bladder cancer from histological images stained via immunohistochemical techniques. Specifically, we propose a novel Deep Convolutional Embedded Attention Clustering (DCEAC) which allows classifying histological pat…
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…
James A. Haley Veterans’ Hospital（詹姆斯·A·黑利退伍军人医院）；University of South Florida（南佛罗里达大学）；Moffitt Cancer Center（莫菲特癌症中心）
The field of Machine Learning, a subset of Artificial Intelligence, has led to remarkable advancements in many areas, including medicine. Machine Learning algorithms require large datasets to train computer models successfully. Although there are medical image datasets available, more image datasets are needed from a variety of medical entities, especially cancer pathology. Even more scarce are ML-ready image datasets. To address this need, we created an image dataset (LC25000) with 25,000 color images in 5 classes. Each class contains 5,000 images of the following histologic entities: colon a…
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…
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…
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…
Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice. State of the art methods rely on hand-crafted annotations which hinder clinical translation since histology suffers from significant variations between cancer phenotypes. In this paper, we propose a weakly supervised framework for whole slide imaging segmentation that relies on standard clinical annotations, available in most medical systems. In particular, we exploit a multiple instance learning scheme for training models. The proposed framework h…
Indian Institute of Information Technology Allahabad（阿拉哈巴德印度信息技术学院）
The use of Deep Learning (DL) based methods in medical histopathology images have been one of the most sought after solutions to classify, segment, and detect diseased biopsy samples. However, given the complex nature of medical datasets due to the presence of intra-class variability and heterogeneity, the use of complex DL models might not give the optimal performance up to the level which is suitable for assisting pathologists. Therefore, ensemble DL methods with the scope of including domain agnostic handcrafted Features (HC-F) inspired this work. We have, through experiments, tried to high…
Norwegian University of Science and Technology（挪威科技大学）；St. Olavs Hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；SINTEF Digital（挪威科技工业研究院数字研究所）
Histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for semantic segmentation of gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolution…
Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types
Institute for Cancer Genetics and Informatics（癌症基因组学与信息学研究所）；Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；Department of Gastrointestinal（胃肠科部）；Institute of Clinical Medicine（临床医学研究所）；Department of Gynaecological Oncology（妇科肿瘤科部）；Amsterdam University Medical Centres（阿姆斯特丹大学医学中心）；Department of Obstetrics and Gynaecology（妇产科部）；Comprehensive Cancer Center Innsbruck（因斯布鲁克综合癌症中心）；Innsbruck Medical University（因斯布鲁克医学院）；Vestre Viken Hospital Trust（维斯特维肯医院信托）；Department of Medical Biology（医学生物学部）；UiT The Arctic University of Norway（挪威北极大学（UiT））；Department of Clinical Pathology（临床病理学部）
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…
This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors. Other state-of-theart works mainly focus on fully supervised learning approaches that rely heavily on human annotations. However, the scarcity of labeled and unlabeled data is a long-standing challenge in histopathology. Currently, representation learning without labels remains unexplored for the histopathology domain. The proposed method, Magnification Prior Contrastive Similarity (MPCS), enables self-supervised lear…
DCT-HistoTransformer: Efficient Lightweight Vision Transformer with DCT Integration for histopathological image analysis
Department of Medical Engineering, Electrical Engineering Department, Sharif University of Technology（谢赫·伊斯兰技术大学医学工程系、电气工程系）；Department of Mathematical and Computer Sciences, Kharazmi University（卡拉齐米大学数学与计算机科学系）
ETH Zurich（苏黎世联邦理工学院）；EPFL（洛桑联邦理工学院）；IBM Research（IBM研究院）；IRCCS-Fondazione Pascale（IRCCS-帕斯卡尔基金会）；ICAR-CNR（意大利国家研究理事会ICAR研究所）
Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue. Recent approaches acknowledging the structure-function relationship, have linked the structural and spatial patterns of cell organization in tissue via cell-graphs to tumor grades. Though cell organization is imperative, it is insufficient to entirely represent the histopathological structure. We propose a novel hierarchical cell-to-tissue-graph (HACT) representation to improve the structural depiction of the tissue…
A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble Method
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 …
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…
Purpose: We aimed to develop deep machine learning (DL) models to improve the detection and segmentation of intraprostatic lesions (IL) on bp-MRI by using whole amount prostatectomy specimen-based delineations. We also aimed to investigate whether transfer learning and self-training would improve results with small amount labelled data. Methods: 158 patients had suspicious lesions delineated on MRI based on bp-MRI, 64 patients had ILs delineated on MRI based on whole mount prostatectomy specimen sections, 40 patients were unlabelled. A non-local Mask R-CNN was proposed to improve the segmentat…
