Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network

Histology imaging is an essential diagnosis method to finalize the grade and stage of cancer of different tissues, especially for breast cancer diagnosis. Specialists often disagree on the final diagnosis on biopsy tissue due to the complex morphological variety. Although convolutional neural networks (CNN) have advantages in extracting discriminative features in image classification, directly training a CNN on high resolution histology images is computationally infeasible currently. Besides, inconsistent discriminative features often distribute over the whole histology image, which incurs challenges in patch-based CNN classification method. In this paper, we propose a novel architecture for automatic classification of high resolution histology images. First, an adapted residual network is employed to explore hierarchical features without attenuation. Second, we develop a robust deep fusion network to utilize the spatial relationship between patches and learn to correct the prediction bias generated from inconsistent discriminative feature distribution. The proposed method is evaluated using 10-fold cross-validation on 400 high resolution breast histology images with balanced labels and reports 95% accuracy on 4-class classification and 98.5% accuracy, 99.6% AUC on 2-class classification (carcinoma and non-carcinoma), which substantially outperforms previous methods and close to pathologist performance.Comment: 8 pages, MICCAI workshop preceeding

Association between Radiologists' Experience and Accuracy in Interpreting Screening Mammograms

<p>Abstract</p> <p>Background</p> <p>Radiologists have been observed to differ, sometimes substantially, both in their interpretations of mammograms and in their recommendations for follow-up. The aim of this study was to determine how factors related to radiologists' experience affect the accuracy of mammogram readings.</p> <p>Methods</p> <p>We selected a random sample of screening mammograms from a population-based breast cancer screening program. The sample was composed of 30 women with histopathologically-confirmed breast cancer and 170 women without breast cancer after a 2-year follow-up (the proportion of cancers was oversampled). These 200 mammograms were read by 21 radiologists routinely interpreting mammograms, with different amount of experience, and by seven readers who did not routinely interpret mammograms. All readers were blinded to the results of the screening. A positive assessment was considered when a BI-RADS III, 0, IV, V was reported (additional evaluation required). Diagnostic accuracy was calculated through sensitivity and specificity.</p> <p>Results</p> <p>Average specificity was higher in radiologists routinely interpreting mammograms with regard to radiologists who did not (66% vs 56%; p < .001). Multivariate analysis based on routine readers alone showed that specificity was higher among radiologists who followed-up cases for which they recommended further workup (feedback) (OR 1.37; 95% CI 1.03 to 1.85), those spending less than 25% of the working day on breast radiology (OR 1.49; 95% CI 1.18 to 1.89), and those aged more than 45 years old (OR 1.33; 95% CI 1.12 to 1.59); the variable of average annual volume of mammograms interpreted by radiologists, classified as more or less than 5,000 mammograms per year, was not statistically significant (OR 1.06; 95% CI 0.90 to 1.25).</p> <p>Conclusion</p> <p>Among radiologists who read routinely, volume is not associated with better performance when interpreting screening mammograms, although specificity decreased in radiologists not routinely reading mammograms. Follow-up of cases for which further workup is recommended might reduce variability in mammogram readings and improve the quality of breast cancer screening programs.</p

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018Comment: 8 pages, 4 figure

The inter-observer agreement of examining pre-school children with acute cough: a nested study

BACKGROUND: The presence of clinical signs have implications for diagnosis, prognosis and treatment. Therefore, the aim of this study was to examine the inter-observer agreement of clinical signs in pre-school children presenting to primary care. METHODS: A nested study comparing two clinical assessments within a prospective cohort of 256 pre-school children with acute cough recruited from eight general practices in Leicestershire, UK. We examined agreement (using kappa statistics) between unstandardised and standardised clinical assessments of tachypnoea, chest signs and fever. RESULTS: Kappa values were poor or fair for all clinical signs (range 0.12 to 0.39) with chest signs the most reliable. CONCLUSIONS: Primary care clinicians should be aware that clinical signs may be unreliable when making diagnosis, prognosis and treatment decisions in pre-school children with cough. Future research should aim to further our understanding of how best to identify abnormal clinical signs

‘Should a mammographic screening programme carry the warning: Screening can damage your health!’?

The balanced presentation afforded by convening a Citizens' Jury when considering a major question such as the introduction of a breast screening programme is advocated. This method would enable account to be taken of all the costs, both human and financial, to all those affected, both participating and organizing, as well as the benefits. Provision of such a democratic opportunity enables consideration to be given to a broad range of factors, by selection of an appropriate range of witnesses, with the advantage of involving the lay public in this decision-making process. Attendance by health correspondents, medical journalists and other media representatives enables publicization of a democracy in action whilst helping to inform the wider debate. Such an exercise could inform whether the NHS BSP should continue in its current form. © 1999 Cancer Research Campaig

Monitoring Temporal Changes in the Specificity of an Oral HIV Test: A Novel Application for Use in Postmarketing Surveillance

BACKGROUND: Postmarketing surveillance is routinely conducted to monitor performance of pharmaceuticals and testing devices in the marketplace. However, these surveillance methods are often done retrospectively and, as a result, are not designed to detect issues with performance in real-time. METHODS AND FINDINGS: Using HIV antibody screening test data from New York City STD clinics, we developed a formal, statistical method of prospectively detecting temporal clusters of poor performance of a screening test. From 2005 to 2008, New York City, as well as other states, observed unexpectedly high false-positive (FP) rates in an oral fluid-based rapid test used for screening HIV. We attempted to formally assess whether the performance of this HIV screening test statistically deviated from both local expectation and the manufacturer's claim for the test. Results indicate that there were two significant temporal clusters in the FP rate of the oral HIV test, both of which exceeded the manufacturer's upper limit of the 95% CI for the product. Furthermore, the FP rate of the test varied significantly by both STD clinic and test lot, though not by test operator. CONCLUSIONS: Continuous monitoring of surveillance data has the benefit of providing information regarding test performance, and if conducted in real-time, it can enable programs to examine reasons for poor test performance in close proximity to the occurrence. Techniques used in this study could be a valuable addition for postmarketing surveillance of test performance and may become particularly important with the increase in rapid testing methods

The utility of ductal lavage in breast cancer detection and risk assessment

Ductal lavage (DL) permits noninvasive retrieval of epithelial cells from the breast. Clinical development of this technique has been fueled largely by its potential, as yet unproven, to improve detection of breast cancer and definition of individual risk for development of breast cancer. Early studies demonstrate the feasibility of performing this technique, provide data on cellular yield and findings, and demonstrate the ability to measure molecular markers in DL fluid. However, the sensitivity and specificity of DL for the detection of breast cancer remains unknown, as does the significance of atypia, particularly mild atypia, when found in DL fluid. Although DL appears safe and the device is approved by the US Food and Drug Administration, DL is still best utilized in the setting of clinical trials designed to resolve issues of sensitivity, specificity, and localization

TriResNet: A Deep Triple-stream Residual Network for Histopathology Grading

While microscopic analysis of histopathological slides is generally considered as the gold standard method for performing cancer diagnosis and grading, the current method for analysis is extremely time consuming and labour intensive as it requires pathologists to visually inspect tissue samples in a detailed fashion for the presence of cancer. As such, there has been significant recent interest in computer aided diagnosis systems for analysing histopathological slides for cancer grading to aid pathologists to perform cancer diagnosis and grading in a more efficient, accurate, and consistent manner. In this work, we investigate and explore a deep triple-stream residual network (TriResNet) architecture for the purpose of tile-level histopathology grading, which is the critical first step to computer-aided whole-slide histopathology grading. In particular, the design mentality behind the proposed TriResNet network architecture is to facilitate for the learning of a more diverse set of quantitative features to better characterize the complex tissue characteristics found in histopathology samples. Experimental results on two widely-used computer-aided histopathology benchmark datasets (CAMELYON16 dataset and Invasive Ductal Carcinoma (IDC) dataset) demonstrated that the proposed TriResNet network architecture was able to achieve noticeably improved accuracies when compared with two other state-of-the-art deep convolutional neural network architectures. Based on these promising results, the hope is that the proposed TriResNet network architecture could become a useful tool to aiding pathologists increase the consistency, speed, and accuracy of the histopathology grading process.Comment: 9 page

A case–control study of the impact of the East Anglian breast screening programme on breast cancer mortality

Although breast cancer screening has been shown to work in randomised trials, there is a need to evaluate service screening programmes to ensure that they are delivering the benefit indicated by the trials. We carried out a case–control study to investigate the effect of mammography service screening, in the NHS breast screening programme, on breast cancer mortality in the East Anglian region of the UK. Cases were deaths from breast cancer in women diagnosed between the ages of 50 and 70 years, following the instigation of the East Anglia Breast Screening Programme in 1989. The controls were women (two per case) who had not died of breast cancer, from the same area, matched by date of birth to the cases. Each control was known to be alive at the time of death of her matched case. All women were known to the breast screening programme and were invited, at least once, to be screened. There were 284 cases and 568 controls. The odds ratio (OR) for risk of death from breast cancer in women who attended at least one routine screen compared to those who did not attend was 0.35 (CI: 0.24, 0.50). Adjusting for self-selection bias gave an estimate of the breast cancer mortality reduction associated with invitation to screening of 35% (OR=0.65, 95% CI: 0.48, 0.88). The effect of actually being screened was a 48% breast cancer mortality reduction (OR=0.52, 95% CI: 0.32, 0.84). The results suggest that the National Breast Screening Programme in East Anglia is achieving a reduction in breast cancer deaths, which is at least consistent with the results from the randomised controlled trials of mammographic screening