Hematoxylin 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.
Prostate 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.
Including uncertainty information in the assessment of a segmentation of pathologic structures on medical images, offers the potential to increase trust into deep learning algorithms for the analysis of medical imaging. Here, we examine options to extract uncertainty information from deep learning segmentation models and the influence of the choice of cost functions on these uncertainty measures. To this end we train conventional UNets without dropout, deep UNet ensembles, and Monte-Carlo (MC) dropout UNets to segment lung nodules on low dose CT using either soft Dice or weighted categorical cross-entropy (wcc) as loss functions. We extract voxel-wise uncertainty information from UNet models based on softmax maximum probability and from deep ensembles and MC dropout UNets using mean voxel-wise entropy. Upon visual assessment, areas of high uncertainty are localized in the periphery of segmentations and are in good agreement with incorrectly labelled voxels. Furthermore, we evaluate how well uncertainty measures correlate with segmentation quality (Dice score). Mean uncertainty over the segmented region (Ulabelled) derived from conventional UNet models does not show a strong quantitative relationship with the Dice score (Spearman correlation coefficient of -0.45 for the soft Dice vs -0.64 for the wcc model respectively). By comparison, image-level uncertainty measures derived from soft Dice as well as wcc MC UNet and deep UNet ensemble models correlate well with the Dice score. In conclusion, using uncertainty information offers ways to assess segmentation quality fully automatically without access to ground truth. Models trained using weighted categorical cross-entropy offer more meaningful uncertainty information on a voxel-level.
The 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.
Radiomics has shown promising results in several medical studies, yet it suffers from a limited discrimination and informative capability as well as a high variation and correlation with the tomographic scanner types, pixel spacing, acquisition protocol, and reconstruction parameters. We propose and compare two methods to transform quantitative image features in order to improve their stability across varying image acquisition parameters while preserving the texture discrimination abilities. In this way, variations in extracted features are representative of true physiopathological tissue changes in the scanned patients. A first approach is based on a two-layer neural network that can learn a nonlinear standardization transformation of various types of features including handcrafted and deep features. Second, domain adversarial training is explored to increase the invariance of the transformed features to the scanner of origin. The generalization of the proposed approach to unseen textures and unseen scanners is demonstrated by a set of experiments using a publicly available computed tomography texture phantom dataset scanned with various imaging devices and parameters.
Radiomics has shown promising results in several medical studies, yet it suffers from a limited discrimination and informative capability as well as a high variation and correlation with the tomographic scanner types, CT (Computer Tomography) scanner producers, pixel spacing, acquisition protocol and reconstruction parameters. This paper introduces a new method to transform image features in order to improve their stability across scanner producers and scanner models. This method is based on a two-layer neural network that can learn a non-linear standardization transformation of various types of features including hand-crafted and deep features. A publicly available database of phantom images with ground truth is used where the same physical phantom was scanned on 17 different CT scanners. In this setting, variations in extracted features are representative of true physio-pathological tissue changes in the scanned patients, so harmonized between scanner producers and models. The recent success of radiomics studies has often been restricted to relatively controlled environments. In order allow for comparing data of several hospitals produced with a larger variety of scanner producers and models as well as with several protocols, features standardization seems necessary to keep results comparable.
Computer-aided diagnosis tools for Retinopathy of Prematurity (ROP) base their decisions on handcrafted retinal features that highly correlate with expert diagnoses, such as arterial and venous curvature, tortuosity and dilation. Deep learning leads to performance comparable to those of expert physicians, albeit not ensuring that the same clinical factors are learned in the deep representations. In this paper, we investigate the relationship between the handcrafted and the deep learning features in the context of ROP diagnosis. Average statistics on the handcrafted features for each input image were expressed as retinal concept measures. Three disease severity grades, i.e. normal, pre-plus and plus, were classified by a deep convolutional neural network. Regression Concept Vectors (RCV) were computed in the network feature space for each retinal concept measure. Relevant concept measures were identified by bidirectional relevance scores for the normal and plus classes. Results show that the curvature, diameter and tortuosity of the segmented vessels are indeed relevant to the classification. Among the potential applications of this method, the analysis of borderline cases between the classes and of network faults, in particular, can be used to improve the performance.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.