Abstract
The automation of interictal epileptiform discharges through deep learning models can increase assertiveness and reduce the time spent on epilepsy diagnosis, making the process faster and more reliable. It was demonstrated that deep sequence networks can be a useful type of algorithm to effectively detect IEDs. Several different deep networks were tested, of which the best three architectures reached average AUC values of 0.96, 0.95 and 0.94, with convergence of test specificity and sensitivity values around 90%, which indicates a good ability to detect IED samples in EEG records.

Abstract

Abstract
Facial expressions are one of the most common way to externalize our emotions. However, the same emotion can have different effects on the same person and has different effects on different people. Based on this, we developed a system capable of detecting the facial expressions of a person in real-time, occluding the eyes (simulating the use of virtual reality glasses). To estimate the position of the eyes, in order to occlude them, Multi-task Cascade Convolutional Neural Networks (MTCNN) were used. A residual network, a VGG, and the combination of both models, were used to perform the classification of 7 different types of facial expressions (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral), classifying the occluded and non-occluded dataset. The combination of both models, achieved an accuracy of 64.9% for the occlusion dataset and 62.8% for no occlusion, using the FER-2013 dataset. The primary goal of this work was to evaluate the influence of occlusion, and the results show that the majority of the classification is done with the mouth and chin. Nevertheless, the results were far from the state-of-the-art, which is expect to be improved, mainly by adjusting the MTCNN.

Abstract

Abstract
Antibiotic resistance is a major health concern of the 21st century. The misuse of antibiotics over the years has led to their increasing presence in the environment, particularly in water resources, which can exacerbate the transmission of resistance genes and facilitate the emergence of resistant microorganisms. The objective of the present work is to develop a chemosensor for screening of sulfonamides in environmental waters, targeting sulfamethoxazole as the model analyte. The methodology was based on the retention of sulfamethoxazole in disks containing polystyrene divinylbenzene sulfonated sorbent particles and reaction with p-dimethylaminocinnamaldehyde, followed by colorimetric detection using a computer-vision algorithm. Several color spaces (RGB, HSV and CIELAB) were evaluated, with the coordinate a_star, from the CIELAB color space, providing the highest sensitivity. Moreover, in order to avoid possible errors due to variations in illumination, a color palette is included in the picture of the analytical disk, and a correction using the a_star value from one of the color patches is proposed. The methodology presented recoveries of 82-101% at 0.1 mu g and 0.5 mu g of sulfamethoxazole (25 mL), providing a detection limit of 0.08 mu g and a quantification limit of 0.26 mu g. As a proof of concept, application to in-field analysis was successfully implemented.

Abstract
Lung cancer is one of the most common causes of cancer-related mortality, and since the majority of cases are diagnosed when the tumor is in an advanced stage, the 5-year survival rate is dismally low. Nevertheless, the chances of survival can increase if the tumor is identified early on, which can be achieved through screening with computed tomography (CT). The clinical evaluation of CT images is a very time-consuming task and computed-aided diagnosis systems can help reduce this burden. The segmentation of the lungs is usually the first step taken in image analysis automatic models of the thorax. However, this task is very challenging since the lungs present high variability in shape and size. Moreover, the co-occurrence of other respiratory comorbidities alongside lung cancer is frequent, and each pathology can present its own scope of CT imaging appearances. This work investigated the development of a deep learning model, whose architecture consists of the combination of two structures, a U-Net and a ResNet34. The proposed model was designed on a cross-cohort dataset and it achieved a mean dice similarity coefficient (DSC) higher than 0.93 for the 4 different cohorts tested. The segmentation masks were qualitatively evaluated by two experienced radiologists to identify the main limitations of the developed model, despite the good overall performance obtained. The performance per pathology was assessed, and the results confirmed a small degradation for consolidation and pneumocystis pneumonia cases, with a DSC of 0.9015 +/- 0.2140 and 0.8750 +/- 0.1290, respectively. This work represents a relevant assessment of the lung segmentation model, taking into consideration the pathological cases that can be found in the clinical routine, since a global assessment could not detail the fragilities of the model.