Abstract
Lung cancer is one of the most common causes of death in the world. The early detection of lung nodules allows an appropriate follow-up, timely treatment and potentially can avoid greater damage in the patient health. The texture is one of the nodule characteristics that is correlated with the malignancy. We developed convolutional neural network architectures to classify automatically the texture of nodules into the non-solid, part-solid and solid classes. The different architectures were tested to determine if the context, the number of slices considered as input and the relation between slices influence on the texture classification performance. The architecture that obtained better performance took into account different scales, different rotations and the context of the nodule, obtaining an accuracy of 0.833 ± 0.041.

Abstract
Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future.

Abstract
The segmentation and characterization of the ovarian structures are important tasks in gynecological and reproductive medicine. Ultrasound imaging is typically used for the medical diagnosis within this field but the understanding of the images can be difficult due to their characteristics. Furthermore, the complexity of ultrasound data may lead to a heavy image processing, which makes the application of classical methods of computer vision difficult. This work presents the first supervised fully convolutional neural network (fCNN) for the automatic segmentation of ovarian structures in B-mode ultrasound images. Due to the small dataset available, only 57 images were used for training. In order to overcome this limitation, several regularization techniques were used and are discussed in this paper. The experiments show the ability of the fCNN to learn features to distinguish ovarian structures, achieving a Dice similarity coefficient (DSC) of 0.855 for the segmentation of the stroma and a DSC of 0.955 for the follicles. When compared with a semi-automatic commercial application for follicle segmentation, the proposed fCNN achieved an average improvement of 19%.

Abstract
Lung cancer diagnosis is made by radiologists through nodule search in chest Computed Tomography (CT) scans. This task is known to be difficult and prone to errors that can lead to late diagnosis. Although Computer-Aided Diagnostic (CAD) systems are promising tools to be used in clinical practice, experienced radiologists continue to perform better diagnosis than CADs. This paper proposes a methodology for characterizing the radiologist's gaze during nodules search in chest CT scans. The main goals are to identify regions that attract the radiologists' attention, which can then be used for improving a lung CAD system, and to create a tool to assist radiologists during the search task. For that purpose, the methodology processes the radiologists' gaze and their mouse coordinates during the nodule search. The resulting data is then processed to obtain a 3D gaze path from which relevant attention studies can be derived. To better convey the found information, a reference model of the lung that eases the communication of the location of relevant anatomical/pathological findings is also proposed. The methodology is tested on a set of 24 real-practice gazes, recorded via an Eye tracker, from 3 radiologists.

Abstract
Lung cancer has an increasing preponderance in worldwide mortality, demanding for the development of efficient screening methods. With this in mind, a binary classification method using Lung-RADS (TM) guidelines to warn changes in the screening management is proposed. First, having into account the lack of public datasets for this task, the lung nodules in the LIDC-IDRI dataset were re-annotated to include a Lung-RADS (TM)-based referral label. Then, a wide residual network is used for automatically assessing lung nodules in 3D chest computed tomography exams. Unlike the standard malignancy prediction approaches, the proposed method avoids the need to segment and characterize lung nodules, and instead directly defines if a patient should be submitted for further lung cancer tests. The system achieves a nodule-wise accuracy of 0.87 +/- 0.02.

Abstract
This study describes a novel dataset with retinal image quality annotation, defined by three different retinal experts, and presents an inter-observer analysis for quality assessment that can be used as gold-standard for future studies. A state-of-the-art algorithm for retinal image quality assessment is also analysed and compared against the specialists performance. Results show that, for 71% of the images present in the dataset, the three experts agree on the given image quality label. The results obtained for accuracy, specificity and sensitivity when comparing one expert against another were in the ranges [83.0 - 85.2]%, [72.7 - 92.9]% and [80.0 - 94.7]%, respectively. The evaluated automatic quality assessment method, despite not being trained on the novel dataset, presents a performance which is within inter-observer variability.