Abstract

Abstract
The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. Recent studies suggest that deep learning can be used to assess CXRs, providing an important second opinion for radiologists and technicians in the decision process, and super-human performance in detection of COVID-19 has been reported in multiple studies. In this study, the clinical applicability of deep learning systems for COVID-19 screening was assessed by testing the performance of deep learning systems for the detection of COVID-19. Specifically, four datasets were used: (1) a collection of multiple public datasets (284.793 CXRs); (2) BIMCV dataset (16.631 CXRs); (3) COVIDGR (852 CXRs) and 4) a private dataset (6.361 CXRs). All datasets were collected retrospectively and consist of only frontal CXR views. A ResNet-18 was trained on each of the datasets for the detection of COVID-19. It is shown that a high dataset bias was present, leading to high performance in intradataset train-test scenarios (area under the curve 0.55-0.84 on the collection of public datasets). Significantly lower performances were obtained in interdataset train-test scenarios however (area under the curve > 0.98). A subset of the data was then assessed by radiologists for comparison to the automatic systems. Finetuning with radiologist annotations significantly increased performance across datasets (area under the curve 0.61-0.88) and improved the attention on clinical findings in positive COVID-19 CXRs. Nevertheless, tests on CXRs from different hospital services indicate that the screening performance of CXR and automatic systems is limited (area under the curve < 0.6 on emergency service CXRs). However, COVID-19 manifestations can be accurately detected when present, motivating the use of these tools for evaluating disease progression on mild to severe COVID-19 patients.

Abstract
Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly and time-consuming. This has fuelled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules. In spite of promising results, the application of automatic methods to clinical routine is not straightforward and only a limited number of studies have addressed the problem in a holistic way. With the goal of advancing the state of the art, the Lung Nodule Database (LNDb) Challenge on automatic lung cancer patient management was organized. The LNDb Challenge addressed lung nodule detection, segmentation and characterization as well as prediction of patient follow-up according to the 2017 Fleischner society pulmonary nodule guidelines. 294 CT scans were thus collected retrospectively at the Centro Hospitalar e Universitrio de So Joo in Porto, Portugal and each CT was annotated by at least one radiologist. Annotations comprised nodule centroids, segmentations and subjective characterization. 58 CTs and the corresponding annotations were withheld as a separate test set. A total of 947 users registered for the challenge and 11 successful submissions for at least one of the sub-challenges were received. For patient follow-up prediction, a maximum quadratic weighted Cohen's kappa of 0.580 was obtained. In terms of nodule detection, a sensitivity below 0.4 (and 0.7) at 1 false positive per scan was obtained for nodules identified by at least one (and two) radiologist(s). For nodule segmentation, a maximum Jaccard score of 0.567 was obtained, surpassing the interobserver variability. In terms of nodule texture characterization, a maximum quadratic weighted Cohen's kappa of 0.733 was obtained, with part solid nodules being particularly challenging to classify correctly. Detailed analysis of the proposed methods and the differences in performance allow to identify the major challenges remaining and future directions data collection, augmentation/generation and evaluation of under-represented classes, the incorporation of scan-level information for better decision making and the development of tools and challenges with clinical-oriented goals. The LNDb Challenge and associated data remain publicly available so that future methods can be tested and benchmarked, promoting the development of new algorithms in lung cancer medical image analysis and patient followup recommendation.

Abstract
Lung cancer is the type of cancer with highest mortality worldwide. Low-dose computerized tomography is the main tool used for lung cancer screening in clinical practice, allowing the visualization of lung nodules and the assessment of their malignancy. However, this evaluation is a complex task and subject to inter-observer variability, which has fueled the need for computer-aided diagnosis systems for lung nodule malignancy classification. While promising results have been obtained with automatic methods, it is often not straightforward to determine which features a given model is basing its decisions on and this lack of explainability can be a significant stumbling block in guaranteeing the adoption of automatic systems in clinical scenarios. Though visual malignancy assessment has a subjective component, radiologists strongly base their decision on nodule features such as nodule spiculation and texture, and a malignancy classification model should thus follow the same rationale. As such, this study focuses on the characterization of lung nodules as a means for the classification of nodules in terms of malignancy. For this purpose, different model architectures for nodule characterization are proposed and compared, with the final goal of malignancy classification. It is shown that models that combine direct malignancy prediction with specific branches for nodule characterization have a better performance than the remaining models, achieving an Area Under the Curve of 0.783. The most relevant features for malignancy classification according to the model were lobulation, spiculation and texture, which is found to be in line with current clinical practice.

Abstract
The detection of ovarian structures from ultrasound images is an important task in gynecological and reproductive medicine. An automatic detection system of ovarian structures can work as a second opinion for less experienced physicians or complex ultrasound interpretations. This work presents a study of three popular CNN-based object detectors applied to the detection of healthy ovarian structures, namely ovary and follicles, in B-mode ultrasound images. The Faster R-CNN presented the best results, with a precision of 95.5% and a recall of 94.7% for both classes, being able to detect all the ovaries correctly. The RetinaNet showed competitive results, exceeding 90% of precision and recall. Despite being very fast and suitable for real-time applications, YOLOv3 was ineffective in detecting ovaries and had the worst results detecting follicles. We also compare CNN results with classical computer vision methods presented in the ovarian follicle detection literature.