Abstract

Abstract

Abstract
Proliferative diabetic retinopathy (PDR) is an advanced diabetic retinopathy stage, characterized by neovascularization, which leads to ocular complications and severe vision loss. However, the available DR-labeled retinal image datasets have a small representation of images of the severest DR grades, and thus there is lack of PDR cases for training DR grading models. Additionally, the criteria for labelling these images in the publicly available datasets is not always clear, with some images which do not show typical PDR lesions being labeled as PDR due to the presence of photo-coagulation treatment and laser marks. This problem, together with the datasets' high class imbalance, leads to a limited variability of the samples, which the typical data augmentation and class balancing cannot fully mitigate. We propose a heuristic-based data augmentation scheme based on the synthesis of neovessel (NV)-like structures that compensates for the lack of PDR cases in DR-labeled datasets. The proposed neovessel generation algorithm relies on the general knowledge of common location and shape of these structures. NVs are generated and introduced in pre-existent retinal images which can then be used for enlarging deep neural networks' training sets. The data augmentation scheme was tested on multiple datasets, and allows to improve the model's capacity to detect NVs.

Abstract
The present work aims at developing a smart dental implant meant to restore the proprioceptive control of the masticatory muscle activity, in consequence of the loss of natural teeth. When periodontal afferent information is not available, the control of the occlusal forces is impaired and the capacity of regulating the masticatory force on a certain tooth or teeth is affected. The active implant being proposed detects the force exerted on teeth and proportionally generates stimuli to send that information to the brain in order to restore the neurobiological mechanisms associated to the masticatory sensory-motor function. After the description of the physiological and biomechanical aspects related to the loss of teeth and masticatory function, details are provided on the force sensing, processing and stimuli generation circuits included in the active implant being proposed. Preliminary simulation results that illustrate the implant functionality are presented.

Abstract
The lung cancer diagnosis is based on the search of lung nodules. Besides its characterization, it is also common to register the anatomical position of these findings. Even though computed-aided diagnosis systems tend to help in these tasks, there is still lacking a complete system that can qualitatively label the nodules in lung regions. In this way, this paper proposes an automatic lung reference model to facilitate the report of nodules between computed-aided diagnosis systems and the radiologist, and among radiologists. The model was applied to 115 computed tomography scans with manually and automatically segmented lobes, and the obtained sectors' variability was evaluated. As the sectors average variability within lobes is less or equal to 0.14, the model can be a good way to promote the report of lung nodules.

Abstract
Active learning (AL) methods create an optimized labeled training set from unlabeled data. We introduce a novel online active deep learning method for medical image analysis. We extend our MedAL AL framework to present new results in this paper. A novel sampling method queries the unlabeled examples that maximize the average distance to all training set examples. Our online method enhances performance of its underlying baseline deep network. These novelties contribute to significant performance improvements, including improving the model's underlying deep network accuracy by 6.30%, using only 25% of the labeled dataset to achieve baseline accuracy, reducing backpropagated images during training by as much as 67%, and demonstrating robustness to class imbalance in binary and multiclass tasks. This article is categorized under: Technologies > Machine Learning Technologies > Classification Application Areas > Health Care