Background and objective: Prostate cancer is one of the most common male tumors. The increasing use of whole slide digital scanners has led to an enormous interest in the application of machine learning techniques to histopathological image classification. Here we introduce a novel family of morphological descriptors which, extracted in the appropriate image space and combined with shallow and deep Gaussian process based classifiers, improves early prostate cancer diagnosis. Method: We decompose the acquired RGB image in its RGB and optical density hematoxylin and eosin components. Then, we define two novel granulometry-based descriptors which work in both, RGB and optical density, spaces but perform better when used on the latter. In this space they clearly encapsulate knowledge used by pathologists to identify cancer lesions. The obtained features become * Corresponding author. The first two authors contributed equally.

The volume of labeled data is often the primary determinant of success in developing machine learning algorithms. This has increased interest in methods for leveraging crowds to scale data labeling efforts, and methods to learn from noisy crowd-sourced labels. The need to scale labeling is acute but particularly challenging in medical applications like pathology, due to the expertise required to generate quality labels and the limited availability of qualified experts. In this paper we investigate the application of Scalable Variational Gaussian Processes for Crowdsourcing (SVGPCR) in digital pathology. We compare SVGPCR with other crowdsourcing methods using a large multi-rater dataset where pathologists, pathology residents, and medical students annotated tissue regions breast cancer. Our study shows that SVGPCR is competitive with equivalent methods trained using gold-standard pathologist generated labels, and that SVGPCR meets or exceeds the performance of other crowdsourcing methods based on deep learning. We also show how SVGPCR can effectively learn the class-conditional reliabilities of individual annotators and demonstrate that Gaussian-process classifiers have comparable performance to similar deep learning methods. These results suggest that SVGPCR can meaningfully engage non-experts in pathology labeling tasks, and that the class-conditional reliabilities estimated by SVGPCR may assist in matching annotators to tasks where they perform well.

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We develop a model that allows us to compare the three most common alternatives used for financing wind projects, namely, (i) project finance debt, (ii) project bonds and (iii) mini-perm bank debt with potential refinancing at its maturity. The proposed model not only allows us to sort debts according to their convenience to the financial sponsor, but also helps to study the impact of refinancing conditions on the internal rate of return (IRR) for shareholders through a sensitivity analysis that comprehensively analyses those variables that are particularly uncertain in the refinancing process. Our results suggest that project bonds help maximize the leverage of the project, which leads to a positive effect on IRR for shareholders. The second-most attractive financing source is the mini-perm debt, but this fact is strongly dependent on the refinancing conditions. Our model contributes to fill the gap on the effects of the cost of project debt in developing and producing renewable energy.

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