Automatic diagnosis systems capable of handling multiple pathologies are essential in clinical practice. This study focuses on enhancing precise lesion localization, classification and delineation in transurethral resection of bladder tumor (TURBT) to reduce cancer recurrence. Despite deep learning models success, medical applications face challenges like small and limited datasets and poor image characterization, including the absence lack of color/texture modeling. To address these issues, three solutions are proposed: (1) an improved texture-constrained version of the pix2pixHD cGAN for data augmentation, addressing the tradeoff of generating high-quality images with enough stochasticity using the Fréchet Inception Distance (FID) measure. (2) Introducing the Multiple Mask and Boundary Scoring R-CNN (MM&BS R-CNN), a new mask sub-net scheme where multiple masks are generated from the different levels of the mask sub-net pipeline, improving segmentation accuracy by including a new scoring module to refine object boundaries. (3) A novel accelerated training strategy based on the SGD optimizer with the second momentum. Experimental results show significant mAP improvements: the data generation scheme improves by more than 12 %; MM&BS R-CNN proposed architecture is responsible for an improvement of about 1.25 %, and the training algorithm based on the second-order momentum increases mAP by 2-3 %. The simultaneous use of all three proposals improved the state-of-the-art mAP by 17.44 %.

In humans, the system that controls several biomechanical tasks has a modular organization (muscle synergies). While the muscle synergies in human walking were already studied deeply, this phenomenon in robotic system is not so explored and could bring simpler controllers requiring a minor number of parameters. So, the purpose of this work is to acquire and interpret synergies from selected joint signals in order to answer some specific research questions regarding the verification of synergistic control of a biped robot in a walking activity, e.g, the number of synergies, their activation profiles, their mechanical role, their quality to reproduce/control walking motions, etc. The selected robotic system was the Darwin-OP robot when walking both with a model based and a bio-inspired controller. Tests were performed with the simulated and real robot. The results appear to suggest that the extraction of synergies are quite useful for the robotic locomotion, since with only 2 synergies, is possible to reconstruct the signals of the studied joints. The biomechanical role for each determined synergy is described based on the functional transformation between sensorimotor signals and biomechanical output. Besides, the robustness of the determined synergies is verified in the locomotor activation pattern across strides and across speeds when the mechanical requirements do not change to a large extent.