1,720,983 research outputs found
Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content
No full textOpen access medical content databases such as PubMed Central and TCGA offer possibilities to obtain large amounts of images for training deep learning models. Nevertheless, accurate labeling of large-scale medical datasets is not available and poses challenging tasks for using such datasets. Predicting unknown magnification levels and standardize staining procedures is a necessary preprocessing step for using this data in retrieval and classification tasks. In this paper, a CNN-based regression approach to learn the magnification of histopathology images is presented, comparing two deep learning architectures tailored to regress the magnification. A comparison of the performance of the models is done in a dataset of 34,441 breast cancer patches with several magnifications. The best model, a fusion of DenseNet-based CNNs, obtained a kappa score of 0.888. The methods are also evaluated qualitatively on a set of images from biomedical journals and TCGA prostate patches
Generalizing convolution neural networks on stain color heterogeneous data for computational pathology
No full textHematoxylin and Eosin (H&E) are one of the main tissue stains used in histopathology to discriminate between nuclei and extracellular material while performing a visual analysis of the tissue. However, histopathology slides are often characterized by stain color heterogeneity, due to different tissue preparation settings at different pathology institutes. Stain color heterogeneity poses challenges for machine learning-based computational analysis, increasing the difficulty of producing consistent diagnostic results and systems that generalize well. In other words, it is challenging for a deep learning architecture to generalize on stain color heterogeneous data, when the data are acquired at several centers, and particularly if test data are from a center not present in the training data. In this paper, several methods that deal with stain color heterogeneity are compared regarding their capability to solve center-dependent heterogeneity. Systematic and extensive experimentation is performed on a normal versus tumor tissue classification problem. Stain color normalization and augmentation procedures are used while training a convolutional neural networks (CNN) to generalize on unseen data from several centers. The performance is compared on an internal test set (test data from the same pathology institutes as the training set) and an external test set (test data from institutes not included in the training set). This also allows to measure generalization performance. An improved performance is observed when the predictions of the two best-performed stain color normalization methods with augmentation are aggregated. An average AUC and F1-score on external test are observed as 0.892 ± 0.021 and 0.817 ± 0.032 compared to the baseline 0.860 ± 0.027 and 0.772 ± 0.024 respectively
Staining invariant features for improving generalization of deep convolutional neural networks in computational pathology
Get PDFOne of the main obstacles for the implementation of deep convolutional neural networks (DCNNs) in the clinical pathology workflow is their low capability to overcome variability in slide preparation and scanner configuration, that leads to changes in tissue appearance. Some of these variations may not be not included in the training data, which means that the models have a risk to not generalize well. Addressing such variations and evaluating them in reproducible scenarios allows understanding of when the models generalize better, which is crucial for performance improvements and better DCNN models. Staining normalization techniques (often based on color deconvolution and deep learning) and color augmentation approaches have shown improvements in the generalization of the classification tasks for several tissue types. Domain-invariant training of DCNN's is also a promising technique to address the problem of training a single model for different domains, since it includes the source domain information to guide the training toward domain-invariant features, achieving state-of-the-art results in classification tasks. In this article, deep domain adaptation in convolutional networks (DANN) is applied to computational pathology and compared with widely used staining normalization and color augmentation methods in two challenging classification tasks. The classification tasks rely on two openly accessible datasets, targeting Gleason grading in prostate cancer, and mitosis classification in breast tissue. The benchmark of the different techniques and their combination in two DCNN architectures allows us to assess the generalization abilities and advantages of each method in the considered classification tasks. The code for reproducing our experiments and preprocessing the data is publicly available1. Quantitative and qualitative results show that the use of DANN helps model generalization to external datasets. The combination of several techniques to manage color heterogeneity suggests that several methods together, such as color augmentation methods with DANN training, can generalize even further. The results do not show a single best technique among the considered methods, even when combining them. However, color augmentation and DANN training obtain most often the best results (alone or combined with color normalization and color augmentation). The statistical significance of the results and the embeddings visualizations provide useful insights to design DCNN that generalizes to unseen staining appearances. Furthermore, in this work, we release for the first time code for DANN evaluation in open access datasets for computational pathology. This work opens the possibility for further research on using DANN models together with techniques that can overcome the tissue preparation differences across datasets to tackle limited generalization
Exploiting biomedical literature to mine out a large multimodal dataset of rare cancer studies
No full textThe overall lower survival rate of patients with rare cancers can be explained, among other factors, by the limitations resulting from the scarce available information about them. Large biomedical data repositories, such as PubMed Central Open Access (PMC-OA), have been made freely available to the scientific community and could be exploited to advance the clinical assessment of these diseases. A multimodal approach using visual deep learning and natural language processing methods was developed to mine out 15,028 light microscopy human rare cancer images. The resulting data set is expected to foster the development of novel clinical research in this field and help researchers to build resources for machine learning
Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading
No full textUsing medical images recorded in clinical practice has the potential to be a game-changer in the application of machine learning for medical decision support. Thousands of medical images are produced in daily clinical activity. The diagnosis of medical doctors on these images represents a source of knowledge to train machine learning algorithms for scientific research or computer-aided diagnosis. However, the requirement of manual data annotations and the heterogeneity of images and annotations make it difficult to develop algorithms that are effective on images from different centers or sources (scanner manufacturers, protocols, etc.). The objective of this article is to explore the opportunities and the limits of highly heterogeneous biomedical data, since many medical data sets are small and entail a challenge for machine learning techniques. Particularly, we focus on a small data set targeting meningioma grading. Meningioma grading is crucial for patient treatment and prognosis. It is normally performed by histological examination but recent articles showed that it is possible to do it also on magnetic resonance images (MRI), so non-invasive. Our data set consists of 174 T1-weighted MRI images of patients with meningioma, divided into 126 benign and 48 atypical/anaplastic cases, acquired using 26 different MRI scanners and 125 acquisition protocols, which shows the enormous variability in the data set. The performed preprocessing steps include tumor segmentation, spatial image normalization and data augmentation based on color and affine transformations. The preprocessed cases are passed to a carefully trained 2-D convolutional neural network. Accuracy above 74% was obtained, with the high-grade tumor recall above 74%. The results are encouraging considering the limited size and high heterogeneity of the data set. The proposed methodology can be useful for other problems involving classification of small and highly heterogeneous data sets
Studying Public Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction
No full textMedical imaging research has long suffered problems getting access to large collections of images due to privacy constraints and to high costs that annotating images by physicians causes. With public scientific challenges and funding agencies fostering data sharing, repositories, particularly on cancer research in the US, are becoming available. Still, data and annotations are most often available on narrow domains and specific tasks. The medical literature (particularly articles contained in MedLine) has been used for research for many years as it contains a large amount of medical knowledge. Most analyses have focused on text, for example creating semi-automated systematic reviews, aggregating content on specific genes and their functions, or allowing for information retrieval to access specific content. The amount of research on images from the medical literature has been more limited, as MedLine abstracts are available publicly but no images are included. With PubMed Central, all the biomedical open access literature has become accessible for analysis, with images and text in structured format. This makes the use of such data easier than extracting it from PDF. This article reviews existing work on analyzing images from the biomedical literature and develops ideas on how such images can become useful and usable for a variety of tasks, including finding visual evidence for rare or unusual cases. These resources offer possibilities to train machine learning tools, increasing the diversity of available data and thus possibly the robustness of the classifiers. Examples with histopathology data available on Twitter already show promising possibilities. This article adds links to other sources that are accessible, for example via the ImageCLEF challenges
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Systematic comparison of deep learning strategies for weakly supervised Gleason grading
No full textProstate cancer (PCa) is one of the most frequent cancers in men. Its grading is required before initiating its treatment. The Gleason Score (GS) aims at describing and measuring the regularity in gland patterns observed by a pathologist on the microscopic or digital images of prostate biopsies and prostatectomies. Deep Learning-based (DL) models are the state-of-the-art computer vision techniques for Gleason grading, learning high-level features with high classification power. However, for obtaining robust models with clinical-grade performance, a large number of local annotations are needed. Previous research showed that it is feasible to detect low and high-grade PCa from digitized tissue slides relying only on the less expensive report-level (weakly) supervised labels, thus global rather than local labels. Despite this, few articles focus on classifying the finer-grained GS classes with weakly supervised models. The objective of this paper is to compare weakly supervised strategies for classification of the five classes of the GS from the whole slide image, using the global diagnostic label from the pathology reports as the only source of supervision. We compare different models trained on handcrafted features, shallow and deep learning representations. The training and evaluation are done on the publicly available TCGA-PRAD dataset, comprising of 341 whole slide images of radical prostatectomies, where small patches are extracted within tissue areas and assigned the global report label as ground truth. Our results show that DL networks and class-wise data augmentation outperform other strategies and their combinations, reaching a kappa score of κ = 0.44, which could be further improved with a larger dataset or combining both strong and weakly supervised models
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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