Abstract
Breast cancer is one of the leading causes of female death worldwide. The histological analysis of breast tissue allows for the differentiation of the tissue suspected to be abnormal into four classes: normal tissue, benign tumor, in situ carcinoma and invasive carcinoma. Automatic diagnostic systems can help in that task. In this sense, this work propose a deep neural network approach using transfer learning to classify breast cancer histology images. First, the added top layers are trained and a second fine-tunning is done on some feature extraction layers that are frozen previously. The used network is an Inception Resnet V2. In order to overcome the lack of data, data augmentation is performed too. This work is a suggested solution for the ICIAR 2018 BACH-Challenge and the accuracy is 0.76 in the blind test set. © 2018, Springer International Publishing AG, part of Springer Nature.

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. © Springer Nature Switzerland AG 2019.

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
Central serous chorioretinopathy is a retinal disease in which there is a leak of fluid into the subretinal space resulting in mild to moderate loss of visual acuity. Sequences of images from a fluorescein angiography exam are most of the times used for analyzing these leaks. This work presents a diagnostic aid method to detect and characterize the progression of fluid area along the exam, in order to provide a second opinion and increase the focus and the speed of analysis of the ophthalmologists. The method is based on a comparative approach by image subtraction between the late and early frames. The obtained segmentation results are quite promising with an average Dice coefficient of 0.801 +/- 0.106 for the training set and 0.774 +/- 0.106 for the test set.

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
Early diagnosis of lung cancer via computed tomography can significantly reduce the morbidity and mortality rates associated with the pathology. However, searching lung nodules is a high complexity task, which affects the success of screening programs. Whilst computer-aided detection systems can be used as second observers, they may bias radiologists and introduce significant time overheads. With this in mind, this study assesses the potential of using gaze information for integrating automatic detection systems in the clinical practice. For that purpose, 4 radiologists were asked to annotate 20 scans from a public dataset while being monitored by an eye tracker device, and an automatic lung nodule detection system was developed. Our results show that radiologists follow a similar search routine and tend to have lower fixation periods in regions where finding errors occur. The overall detection sensitivity of the specialists was 0.67 +/- 0.07, whereas the system achieved 0.69. Combining the annotations of one radiologist with the automatic system significantly improves the detection performance to similar levels of two annotators. Filtering automatic detection candidates only for low fixation regions still significantly improves the detection sensitivity without increasing the number of false-positives.