Over the past few years, different Computer-Aided Diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images since there is a strong lack of public clinical images archive available to evaluate the aforementioned CAD systems. To fill this gap, we release a skin lesion benchmark composed of clinical images collected from smartphone devices and a set of patient clinical data containing up to 21 features. The dataset consists of 1373 patients, 1641 skin lesions, and 2298 images for six different diagnostics: three skin diseases and three skin cancers. In total, 58.4% of the skin lesions are biopsy-proven, including 100% of the skin cancers. By releasing this benchmark, we aim to support future research and the development of new tools to assist clinicians to detect skin cancer.

Over the past few years, different computer-aided diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images since there is a strong lack of public clinical images archive available to design them. To fill this gap, we release a skin lesion benchmark composed of clinical images collected from smartphone devices and a set of patient clinical data containing up to 22 features. The dataset consists of 1,373 patients, 1,641 skin lesions, and 2,298 images for six different diagnostics: three skin diseases and three skin cancers. In total, 58.4% of the skin lesions are biopsy-proven, including 100% of the skin cancers. By releasing this benchmark, we aim to aid future research and the development of new tools to assist clinicians to detect skin cancer.

Abductive reasoning algorithms formulate possible hypotheses to explain observed facts using a theory as the basis. These algorithms have been applied to various domains such as diagnosis, planning and interpretation. In general, algorithms for abductive reasoning based on logic present the following disadvantages: (1) they do not allow the explicit declaration of conditions that may affect the reasoning, such as intention, context and belief;(2) they allow little or no consideration for criteria required to select good hypotheses. Using Propositional Logic as its foundation, this study proposes the algorithm Peirce, which operates with a framework that allows one to explicitly include conditions to conduct abductive reasoning and uses a criterion to select good hypotheses that employs metrics to define the explanatory power and complexity of the hypotheses. Experimental results suggest that abductive reasoning performed by humans has the tendency to coincide with the solutions computed by the algorithm Peirce.

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This paper describes TSeg – a Java application that allows for both manual and automatic segmentation of a source text into basic units of annotation. TSeg provides a straightforward way to approach this task through a clear point-and-click interface. Once finished the text segmentation, the application outputs an XML file that may be used as input to a more problem specific annotation software. Hence, TSeg moves the identification of basic units of annotation out of the task of annotating these units, making it possible for both problems to be analysed in isolation, thereby reducing the cognitive load on the user and preventing potential damages to the overall outcome of the annotation process.