Abstract

Abstract
Currently, lung cancer is the most lethal in the world. In order to make screening and follow-up a little more systematic, guidelines have been proposed. Therefore, this study aimed to create a diagnostic support approach by providing a patient label based on the LUNG-RADSTM guidelines. The only input required by the system is the nodule centroid to take the region of interest for the input of the classification system. With this in mind, two deep learning networks were evaluated: a Wide Residual Network and a DenseNet. Taking into account the annotation uncertainty we proposed to use sample weights that are introduced in the loss function, allowing nodules with a high agreement in the annotation process to take a greater impact on the training error than its counterpart. The best result was achieved with the Wide Residual Network with sample weights achieving a nodule-wise LUNG-RADSTM labelling accuracy of 0.735 +/- 0.003.

Abstract
Segmentation process serves to aid the pathology diagnosing process since segmentation filters the interference from other anatomical structures and helps focus on the posterior segment structures of the eye, highlighting a set of signals that will serve for diagnosis of various retinal pathologies. Automatic retinal vessel segmentation can lead to a more accurate diagnosis. This paper presents a framework for automatic vessel segmentation of lower-resolution retinal images taken with a smartphone equipped with D-EYE lens. The framework is evaluated and the attained results were presented. A dataset was assembled and annotated of train models for automatic localisation retinal areas and for vessel segmentation. For the framework, two CNN based models were successfully trained, a Faster R-CNN that achieved a 96% correct detected of all regions with an MAE of 39 pixels, and a U-Net that achieved a DICE of 0.7547. © Springer Nature Switzerland AG 2020.

Abstract
Lung cancer late diagnosis has a large impact on the mortality rate numbers, leading to a very low five-year survival rate of 5%. This issue emphasises the importance of developing systems to support a diagnostic at earlier stages. Clinicians use Computed Tomography (CT) scans to assess the nodules and the likelihood of malignancy. Automatic solutions can help to make a faster and more accurate diagnosis, which is crucial for the early detection of lung cancer. Convolutional neural networks (CNN) based approaches have shown to provide a reliable feature extraction ability to detect the malignancy risk associated with pulmonary nodules. This type of approach requires a massive amount of data to model training, which usually represents a limitation in the biomedical field due to medical data privacy and security issues. Transfer learning (TL) methods have been widely explored in medical imaging applications, offering a solution to overcome problems related to the lack of training data publicly available. For the clinical annotations experts with a deep understanding of the complex physiological phenomena represented in the data are required, which represents a huge investment. In this direction, this work explored a TL method based on unsupervised learning achieved when training a Convolutional Autoencoder (CAE) using images in the same domain. For this, lung nodules from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) were extracted and used to train a CAE. Then, the encoder part was transferred, and the malignancy risk was assessed in a binary classification-benign and malignant lung nodules, achieving an Area Under the Curve (AUC) value of 0.936. To evaluate the reliability of this TL approach, the same architecture was trained from scratch and achieved an AUC value of 0.928. The results reported in this comparison suggested that the feature learning achieved when reconstructing the input with an encoder-decoder based architecture can be considered an useful knowledge that might allow overcoming labelling constraints.

Abstract
The current progress of modern medicine is based on the resistance of malignant tumors in advanced medical treatments, as well as on the need to develop new therapeutic approaches. In the last few years, numerous studies have focused their attention on the promising use of nanomaterials, such as nanowires, zinc oxide, or mesoporous silica nanoparticles, among others. All these particles are studied in the treatment of cancer and metastasis prevention with the advantage of operating directly at the biomolecular scale. These are innovative designs of magnetic nanomaterials based on a core/shell approach that started to gain prominence due to their versatility to tailor properties of both core and shell and to offer multifunctionality, such as core protection, biofunctionalization platform, toxicity reduction, and enhanced biocompatibility. These nanowire structural improvements allow the development of new bioanalytical chemistry and medical diagnostics advanced tools that will bring about a new age of nanotechnology with widespread use of nanowires for biomedical applications.

Abstract
Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.