Gerardo Fernández

Ignacio Hernández Montilla, Alfonso Medela, Paola Pasquali et al.

Teledermatology has become a widely accepted communication method in daily clinical practice, enabling remote care while showing strong agreement with in-person visits. Poor image quality remains an unsolved problem in teledermatology and is a major concern to practitioners, as bad-quality images reduce the usefulness of the remote consultation process. However, research on Image Quality Assessment (IQA) in dermatology is sparse, and does not leverage the latest advances in non-dermatology IQA, such as using larger image databases with ratings from large groups of human observers. In this work, we propose cross-domain training of IQA models, combining dermatology and non-dermatology IQA datasets. For this purpose, we created a novel dermatology IQA database, Legit.Health-DIQA-Artificial, using dermatology images from several sources and having them annotated by a group of human observers. We demonstrate that cross-domain training yields optimal performance across domains and overcomes one of the biggest limitations in dermatology IQA, which is the small scale of data, and leads to models trained on a larger pool of image distortions, resulting in a better management of image quality in the teledermatology process.

Guilherme Aresta, Teresa Araújo, Scotty Kwok et al.

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). A large annotated dataset, composed of both microscopy and whole-slide images, was specifically compiled and made publicly available for the BACH challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. From the submitted algorithms it was possible to push forward the state-of-the-art in terms of accuracy (87%) in automatic classification of breast cancer with histopathological images. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publically available as to promote further improvements to the field of automatic classification in digital pathology.

Cecilia Murrugarra, Osberth De Castro, Juan Carlos Grieco et al.

In this paper we propose an implicit force control scheme for a one-link flexible manipulator that interact with a compliant environment. The controller was based in the mathematical model of the manipulator, considering the dynamics of the beam flexible and the gravitational force. With this method, the controller parameters are obtained from the structural parameters of the beam (link) of the manipulator. This controller ensure the stability based in the Lyapunov Theory. The controller proposed has two closed loops: the inner loop is a tracking control with gravitational force and vibration frequencies compensation and the outer loop is a implicit force control. To evaluate the performance of the controller, we have considered to three different manipulators (the length, the diameter were modified) and three environments with compliance modified. The results obtained from simulations verify the asymptotic tracking and regulated in position and force respectively and the vibrations suppression of the beam in a finite time.

Bernardo Aceituno-Cabezas, Jose Cappelletto, Juan C. Grieco et al.

Robot footstep planning strategies can be divided in two main approaches: discrete searches and continuous optimizations. While discrete searches have been broadly applied, continuous optimizations approaches have been restricted for humanoid platforms. This article introduces a generalized continuous-optimization approach for multilegged footstep planning which can be adapted to different platforms, regardless the number and geometry of legs. This approach leverages Mixed-Integer Convex Programming to account for the non-convex constraints that represent footstep rotation and obstacle avoidance. The planning problem is formulated as an optimization problem which considers robot geometry and reachability with linear constraints, and can be efficiently solved using optimization software. To demonstrate the functionality and adaptability of the planner, a set of tests are performed on a BH3R hexapod and a LittleDog quadruped on scenarios which can't be easily handled with discrete searches, such tests are solved efficiently in fractions of a second. This work represents, to the knowledge of the authors, the first successful implementation of a continuous optimization-based multilegged footstep planner.