5,765 research outputs found

    Histopathological image analysis : a review

    Get PDF
    Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

    Machine learning methods for histopathological image analysis

    Full text link
    Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.Comment: 23 pages, 4 figure

    Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

    Full text link
    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-invariant CNNs. The latter aims to match training and test color distributions in order to reduce stain variation. For the first time, we compared some of these techniques and quantified their effect on CNN classification performance using a heterogeneous dataset of hematoxylin and eosin histopathology images from 4 organs and 9 pathology laboratories. Additionally, we propose a novel unsupervised method to perform stain color normalization using a neural network. Based on our experimental results, we provide practical guidelines on how to use stain color augmentation and stain color normalization in future computational pathology applications.Comment: Accepted in the Medical Image Analysis journa

    Measuring the metastatic potential of cancer cells

    Get PDF
    Cancer cells must secrete proteolytic enzymes to invade adjacent tissues and migrate to a new metastatic site. Urokinase (uPA) is a key enzyme related to metastasis in cancers of the lung, colon, gastric, uterine, breast, brain, and malignant melanoma. A NASA technology utilization project has combined fluorescence microscopy, image analysis, and flow cytometry, using fluorescent dyes, and urokinase-specific antibodies to measure uPA and abnormal DNA levels (related to cancer cell proliferation) inside the cancer cells. The project is focused on developing quantitative measurements to determine if a patient's tumor cells are actively metastasizing. If a significant number of tumor cells contain large amounts of uPA (esp. membrane-bound) then the post-surgical chemotherapy or radiotherapy can be targeted for metastatic cells that have already left the primary tumor. These analytical methods have been applied to a retrospective study of biopsy tissues from 150 node negative, stage 1 breast cancer patients. Cytopathology and image analysis has shown that uPA is present in high levels in many breast cancer cells, but not found in normal breast. Significant amounts of uPA also have been measured in glioma cell lines cultured from brain tumors. Commercial applications include new diagnostic tests for metastatic cells, in different cancers, which are being developed with a company that provides a medical testing service using flow cytometry for DNA analysis and hormone receptors on tumor cells from patient biopsies. This research also may provide the basis for developing a new 'magic bullet' treatment against metastasis using chemotherapeutic drugs or radioisotopes attached to urokinase-specific monoclonal antibodies that will only bind to metastatic cells

    Cancer diagnosis using deep learning: A bibliographic review

    Get PDF
    In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements
    corecore