Saliency-Enhanced Content-Based Image Retrieval for Diagnosis Support in Dermatology Consultation: Reader Study.

BACKGROUND Previous research studies have demonstrated that medical content image retrieval can play an important role by assisting dermatologists in skin lesion diagnosis. However, current state-of-the-art approaches have not been adopted in routine consultation, partly due to the lack of interpretability limiting trust by clinical users. OBJECTIVE This study developed a new image retrieval architecture for polarized or dermoscopic imaging guided by interpretable saliency maps. This approach provides better feature extraction, leading to better quantitative retrieval performance as well as providing interpretability for an eventual real-world implementation. METHODS Content-based image retrieval (CBIR) algorithms rely on the comparison of image features embedded by convolutional neural network (CNN) against a labeled data set. Saliency maps are computer vision-interpretable methods that highlight the most relevant regions for the prediction made by a neural network. By introducing a fine-tuning stage that includes saliency maps to guide feature extraction, the accuracy of image retrieval is optimized. We refer to this approach as saliency-enhanced CBIR (SE-CBIR). A reader study was designed at the University Hospital Zurich Dermatology Clinic to evaluate SE-CBIR's retrieval accuracy as well as the impact of the participant's confidence on the diagnosis. RESULTS SE-CBIR improved the retrieval accuracy by 7% (77% vs 84%) when doing single-lesion retrieval against traditional CBIR. The reader study showed an overall increase in classification accuracy of 22% (62% vs 84%) when the participant is provided with SE-CBIR retrieved images. In addition, the overall confidence in the lesion's diagnosis increased by 24%. Finally, the use of SE-CBIR as a support tool helped the participants reduce the number of nonmelanoma lesions previously diagnosed as melanoma (overdiagnosis) by 53%. CONCLUSIONS SE-CBIR presents better retrieval accuracy compared to traditional CBIR CNN-based approaches. Furthermore, we have shown how these support tools can help dermatologists and residents improve diagnosis accuracy and confidence. Additionally, by introducing interpretable methods, we should expect increased acceptance and use of these tools in routine consultation

Saliency-Enhanced Content-Based Image Retrieval for Diagnosis Support in Dermatology Consultation: Reader Study

BACKGROUND Previous research studies have demonstrated that medical content image retrieval can play an important role by assisting dermatologists in skin lesion diagnosis. However, current state-of-the-art approaches have not been adopted in routine consultation, partly due to the lack of interpretability limiting trust by clinical users. OBJECTIVE This study developed a new image retrieval architecture for polarized or dermoscopic imaging guided by interpretable saliency maps. This approach provides better feature extraction, leading to better quantitative retrieval performance as well as providing interpretability for an eventual real-world implementation. METHODS Content-based image retrieval (CBIR) algorithms rely on the comparison of image features embedded by convolutional neural network (CNN) against a labeled data set. Saliency maps are computer vision-interpretable methods that highlight the most relevant regions for the prediction made by a neural network. By introducing a fine-tuning stage that includes saliency maps to guide feature extraction, the accuracy of image retrieval is optimized. We refer to this approach as saliency-enhanced CBIR (SE-CBIR). A reader study was designed at the University Hospital Zurich Dermatology Clinic to evaluate SE-CBIR's retrieval accuracy as well as the impact of the participant's confidence on the diagnosis. RESULTS SE-CBIR improved the retrieval accuracy by 7% (77% vs 84%) when doing single-lesion retrieval against traditional CBIR. The reader study showed an overall increase in classification accuracy of 22% (62% vs 84%) when the participant is provided with SE-CBIR retrieved images. In addition, the overall confidence in the lesion's diagnosis increased by 24%. Finally, the use of SE-CBIR as a support tool helped the participants reduce the number of nonmelanoma lesions previously diagnosed as melanoma (overdiagnosis) by 53%. CONCLUSIONS SE-CBIR presents better retrieval accuracy compared to traditional CBIR CNN-based approaches. Furthermore, we have shown how these support tools can help dermatologists and residents improve diagnosis accuracy and confidence. Additionally, by introducing interpretable methods, we should expect increased acceptance and use of these tools in routine consultation

Suspicious Skin Lesion Detection in Wide-Field Body Images using Deep Learning Outlier Detection

During consultation dermatologists have to address hundreds of lesions in a limited amount of time. They will not only evaluate the single lesion of interest but more importantly the context of it. Visually comparing the similarity of the majority of lesions within the same patient provides a strong indication for lesions with significantly differing aspects. Deep learning algorithms are capable to identify such outliers, i.e. images that differ considerably from the expected appearance on a larger cohort, and highlight the main differences in those cases. In the present study we evaluate the use of autoencoders as unsupervised tools to detect suspicious skin lesions based on evaluation of real world data acquired during consultation at the USZ Dermatology Clinic. Clinical Relevance— Deep learning algorithms are showing many promising results in dermatology lesion classification. However the context of the lesion is normally not considered in the analysis which prevents these tools to transition into routine practice. An outlier detector based on real world data would allow a dermatologist or general practitioner to detect the suspicious lesions for further examination. The algorithm would additionally provide useful insights by highlighting the feature differences between the original outlier (malignant lesion) and the lesion reconstructed by the autoencode

Nucleated red blood cells as predictors of mortality in patients with acute respiratory distress syndrome (ARDS): an observational study

Abstract Background Nucleated red blood cells (NRBCs) in critically ill patients are associated with increased mortality and poor outcome. The aim of the present study was to evaluate the predictive value of NRBCs in patients with acute respiratory distress syndrome (ARDS). Methods This observational study was conducted at an ARDS referral center and included patients from 2007 to 2014. Daily NRBC counts were assessed and the predictive validity of NRBCs on mortality was statistically evaluated. A cutoff for prediction of mortality based on NRBCs was evaluated using ROC analysis and specified according to Youden’s method. Multivariate nonparametric analysis for longitudinal data was applied to prove for differences between groups over the whole time course. Independent predictors of mortality were identified with multiple logistic and Cox’ regression analyses. Kaplan–Meier estimations visualized the survival; the corresponding curves were tested for differences with the log-rank test. Results A total of 404 critically ill ARDS patients were analyzed. NRBCs were found in 75.5% of the patients, which was associated with longer length of ICU stay [22 (11; 39) vs. 14 (7; 26) days; p  220/µl (OR 1.81; 95% CI 1.1–2.97; p < 0.05). Conclusions NRBCs may predict mortality in ARDS with high prognostic power. The presence of NRBCs in the blood might be regarded as a marker of disease severity indicating a higher risk of ICU death

A Service of zbw Self-fulfilling liquidity dry-ups NBB Working Paper, No. 185 Self-fulfilling liquidity dry-ups No 185

Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Statement of purpose: The purpose of these working papers is to promote the circulation of research results (Research Series) and analytical studies (Documents Series) made within the National Bank of Belgium or presented by external economists in seminars, conferences and conventions organised by the Bank. The aim is therefore to provide a platform for discussion. The opinions expressed are strictly those of the authors and do not necessarily reflect the views of the National Bank of Belgium. Orders For orders and information on subscriptions and reductions: National Bank of Belgium, Documentation -Publications service, boulevard de Berlaimont 14, 1000 Brussels. Abstract Secondary markets for long-term assets might be illiquid due to adverse selection. In a model in which moral hazard is confined to project initiation, I find that: (1) when agents expect a liquidity dryup on such markets, they optimally choose to self-insure through the hoarding of non-productive but liquid assets; (2) such a response has negative externalities as it reduces ex-post market participation, which worsens adverse selection and dries up market liquidity; (3) liquidity dry-ups are Pareto inefficient equilibria; (4) the Government can rule them out. Additionally, when agents face idiosyncratic, privately known, illiquidity shocks, I show that: (5) it increases market liquidity