Abstract
Lung segmentation represents a fundamental step in the development of computer-aided decision systems for the investigation of interstitial lung diseases. In a holistic lung analysis, eliminating background areas from Computed Tomography (CT) images is essential to avoid the inclusion of noise information and spend unnecessary computational resources on non-relevant data. However, the major challenge in this segmentation task relies on the ability of the models to deal with imaging manifestations associated with severe disease. Based on U-net, a general biomedical image segmentation architecture, we proposed a light-weight and faster architecture. In this 2D approach, experiments were conducted with a combination of two publicly available databases to improve the heterogeneity of the training data. Results showed that, when compared to the original U-net, the proposed architecture maintained performance levels, achieving 0.894 +/- 0.060, 4.493 +/- 0.633 and 4.457 +/- 0.628 for DSC, HD and HD-95 metrics, respectively, when using all patients from the ILD database for testing only, while allowing a more efficient computational usage. Quantitative and qualitative evaluations on the ability to cope with high-density lung patterns associated with severe disease were conducted, supporting the idea that more representative and diverse data is necessary to build robust and reliable segmentation tools.

Abstract
The misuse and overuse of antibiotics lead to antibiotic resistance becoming a serious problem and a threat to world health. Bacteria developing resistance results in more dangerous infections and a more difficult treatment. To monitor the antibiotic pollution of environmental waters, different detection methods have been developed, however these are normally complex, costly and time-consuming. In a previous work, we developed a method based on digital colorimetry, using smartphone cameras to acquire sample images and color correction to ensure color constancy between images. A reference chart with 24 colors, with known ground truth values, is included in the photographs in order to color correct the images using least squares minimization. Then, the color of the sample is detected and correlated to antibiotic concentration. Although achieving promising results, the method was too sensitive to contrasting illumination conditions, with high standard deviations in these cases. Here, we test different methods for improving the stability and precision of the previous algorithm. By using only the 13 patches closest to the color of the targets and more parameters for the least squares minimization, better results were achieved, with an improvement of up to 83.33% relative to the baseline. By improving the color constancy, a more precise, less influenced by extreme conditions, estimation of sulfonamides is possible, using a practical and cost-efficient method. © 2021, Springer Nature Switzerland AG.

Abstract
Lung cancer treatments that are accurate and effective are urgently needed. The diagnosis of advanced-stage patients accounts for the majority of the cases, being essential to provide a specialized course of treatment. One emerging course of treatment relies on target therapy through the testing of biomarkers, such as the Epidermal Growth Factor Receptor (EGFR) gene. Such testing can be obtained from invasive methods, namely through biopsy, which may be avoided by applying machine learning techniques to the imaging phenotypes extracted from Computerized Tomography (CT). This study aims to explore the contribution of ensemble methods when applied to the prediction of EGFR mutation status. The obtained results translate in a direct correlation between the semantic predictive model and the outcome of the combined ensemble methods, showing that the utilized features do not have a positive contribution to the predictive developed models.

Abstract
Due to the huge mortality rate of lung cancer, there is a strong need for developing solutions that help with the early diagnosis and the definition of the most appropriate treatment. In the particular case of target therapy, effective genotyping of the tumor is fundamental since this treatment uses targeted drugs that can induce death in cancer cells. The biopsy is the traditional method to assess the genotype information but it is extremely invasive and painful. Medical imaging is a valuable alternative to biopsies, considering the potential to extract imaging features correlated with specific genomic alterations. Regarding the limitations of single model approaches for gene mutation status predictions, ensemble strategies might bring valuable benefits by combining the strengths and weaknesses of the aggregated methods. This preliminary work aims to provide further advances in the radiogenomics field by studying the use of ensemble methods to predict the Epidermal Growth Factor Receptor (EGFR) mutation status in lung cancer. The best result obtained for the proposed ensemble approach was an AUC of 0.706 (± 0.122). However, the ensemble did not outperform the single models with AUC values of 0.712 (± 0.119) for Logistic Regression, 0.711 (± 0.119) for Support Vector Machine and 0.712 (± 0.120) for Elastic Net. The high correlation found on the decisions of each single model might be a plausible explanation for this behavior, which caused the ensemble to misclassify the same examples as the single models.

Abstract
Lung cancer is the deadliest form of cancer, accounting for 20% of total cancer deaths. It represents a group of histologically and molecularly heterogeneous diseases even within the same histological subtype. Moreover, accurate histological subtype diagnosis influences the specific subtype's target genes, which will help define the treatment plan to target those genes in therapy. Deep learning (DL) models seem to set the benchmarks for the tasks of cancer prediction and subtype classification when using gene expression data; however, these methods do not provide interpretability, which is great concern from the perspective of cancer biology since the identification of the cancer driver genes in an individual provides essential information for treatment and prognosis. In this work, we identify some limitations of previous work that showed efforts to build algorithms to extract feature weights from DL models, and we propose using tree-based learning algorithms that address these limitations. Preliminary results show that our methods outperform those of related research while providing model interpretability.

Abstract
Deep Neural Networks using histopathological images as an input currently embody one of the gold standards in automated lung cancer diagnostic solutions, with Deep Convolutional Neural Networks achieving the state of the art values for tissue type classification. One of the main reasons for such results is the increasing availability of voluminous amounts of data, acquired through the efforts employed by extensive projects like The Cancer Genome Atlas. Nonetheless, whole slide images remain weakly annotated, as most common pathologist annotations refer to the entirety of the image and not to individual regions of interest in the patient's tissue sample. Recent works have demonstrated Multiple Instance Learning as a successful approach in classification tasks entangled with this lack of annotation, by representing images as a bag of instances where a single label is available for the whole bag. Thus, we propose a bag/embedding-level lung tissue type classifier using Multiple Instance Learning, where the automated inspection of lung biopsy whole slide images determines the presence of cancer in a given patient. Furthermore, we use a post-model interpretability algorithm to validate our model's predictions and highlight the regions of interest for such predictions.