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Caetano Traina - IEEE Xplore Author Profile

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Given a patient’s series of exams conducted over time, how can we identify cases with similar abnormalities or symptoms? Hospitals and medical facilities continuously monitor patients through periodic exams, a crucial practice for assessing their current condition and potential progression, thereby supporting decision-making. However, similarity-based searches often consider several exams of a pat...Show More
Medical image analysis is an important asset in the clinical process, providing resources to assist physicians in detecting diseases and making accurate diagnoses. Deep Learning (DL) models have been widely applied in these tasks, improving the ability to recognize patterns, including accurate and fast diagnosis. However, DL can present issues related to security violations that reduce the system’...Show More
Deep Learning (DL) is a valuable set of techniques that improve medical decision-making based on imaging exams, such as Chest X-rays (CXR), Computed Tomography (CT), and Optical Coherence Tomography (OCT). However, DL models may be susceptible to adversarial attacks when perturbed (tam-pered) examples sneak into the data, decreasing the model's confidence. In this paper, we evaluate the vulnerabil...Show More
Medical image analysis plays a major role in aiding physicians in decision-making. Specifically in detecting COVID-19, Deep Learning (DL) and radiomic approaches have achieved promising results separately. However, DL results are hard to interpret/visualize, and the radiomic approach encompasses successive steps, such as image acquisition, image processing, segmentation, feature extraction, and an...Show More
Given a large, unlabeled set of Electronic Health Records (EHRs) acquired from multiple hospitals, how can we analyze the available entities and identify relationships in the data? Also, how can we perform Exploratory Data Analysis (EDA) over such EHR data? Many medical institutions generate EHRs as tabular data with entities and attributes in common. However, due to a large number of records, att...Show More
Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians’ daily procedure, especially in developing countries. Electr...Show More
Bone mineral density (BMD) is the international standard for evaluating osteoporosis/osteopenia. The success rate of BMD alone in estimating the risk of vertebral fragility fracture (VFF) is approximately 50%, making BMD far from ideal in predicting VFF. In addition, whether or not a patient has been diagnosed with osteoporosis or osteopenia, he or she may suffer a VFF. For this reason, we conduct...Show More
Osteoporosis is a systemic disorder that reduces the bone mineral density, increasing the vertebrae's fragility and proneness to fracture. Although the bone densitometry index t-Score is a solid marker for the osteoporosis diagnosis, its measure alone is insufficient to predict the future development of fragility fractures. A complementary approach to address vertebral bone characterization is the...Show More
Bone densitometry (DEXA) is the international reference standard to evaluate Bone Mineral Density (BMD) and diagnose osteoporosis. However, DEXA is far from ideal when used to predict fragility fractures, which are strongly related to morbidity and mortality. According to the literature, spine MRI texture features correlate well with DEXA measurements. For this reason, we conducted an extensive em...Show More
Machine learning (ML) algorithms have been used in many areas of activity, and their results can often be applied without further human intervention. The ML algorithms have also been widely used in medical contexts, but in this area, the result needs to be thoroughly confirmed by a specialist, who needs explanatory information on how the results were obtained. Aimed at such scenarios, we propose t...Show More
The current COVID-19 pandemic has promoted the periodic release of several health databases aimed at discovering relationships in the data, detecting similar problems in patients, and studying the evolution of the disease. A way to exploit the data is to use visualization techniques, which can lead to the discovery of insights and patterns, as well as to guide analysis procedures to understand the...Show More
Many patients suffer from chronic skin lesions, commonly known as ulcers. The size evolution of chronic wounds provides meaningful clues regarding the patient's clinical state for healthcare professionals and caretakers. Many studies have been proposed in recent years to support the treatment of skin ulcers. However, there is a lack of practical solutions, as existing studies are not targeted at i...Show More
Several studies have been performed worldwide to improve health services using data generated by digital medical systems. The increasing volume of data generated by these systems is making the use of knowledge discovery and data analysis techniques essential to improve the quality of the health services, which are offered by the medical facilities. However, it is possible to observe a gap, in the ...Show More
The widespread of social networks and online channels has increased the capture of large amounts of complex data, such as images and videos, which demand efficient and flexible tools to perform information retrieval. Many existing approaches to retrieve complex data follow the "Query by Similarity" paradigm, using Metric Access Methods (MAMs) to index complex data and speed-up information retrieva...Show More
Magnetic Resonance Imaging (MRI) is a non-invasive technique, which has been employed to detect and diagnose many spine pathologies. In a Computer-Aided Diagnosis(CAD) context, the segmentation of the paraspinal musculature from MRI may support measurement, quantification, and analysis of muscle-related pathologies. Current semi-automatic seg-mentation techniques require too much time from the phy...Show More
The large amount of medical exams generated by hospitals has a great potential to boost the support for physicians on decision making tasks. This requires efficient and reliable computational systems to retrieve relevant information in real-time. Existing Content-Based Image Retrieval (CBIR) systems rely on Metric Access Methods (MAMs) to speed-up the retrieval task. In this context, images are re...Show More
Segmentation of medical images is critical for making several processes of analysis and classification more reliable. With the growing number of people presenting back pain and related problems, the semi-automatic segmentation and 3D reconstruction of vertebral bodies became even more important to support decision making. A 3D reconstruction allows a fast and objective analysis of each vertebrae c...Show More
Content-based retrieval still remains one of the main problems with respect to controversies and challenges in digital healthcare over big data. To properly address this problem, there is a need for efficient computational techniques, especially in scenarios involving queries across multiple data repositories. In such scenarios, the common computational approach searches the repositories separatel...Show More
This paper describes an upgraded version of the SemIndex prototype system for semantic-aware search in textual SQL databases. Semantic-aware querying has emerged as a required extension of the standard containment keyword-based query to meet user needs in textual databases and IR applications. Here, we build on top of SemIndex, a semantic-aware inverted index previously developed by our team, to a...Show More
Medical exams, such as CT scans and mammograms, are obtained and stored every day in hospitals all over the world, including images, patient data, and medical reports. It is paramount to have tools and systems to improve computer-aided diagnoses based on such huge volumes of stored information. The Content-Based Image Retrieval (CBIR) is a powerful paradigm to help reaching such a goal, providing ...Show More
The images collected during medical exams are a strong asset for diagnosing and decision making. One scenario where clinical images are especially useful is the analysis of chronic lesions on the skin (skin ulcers). The visual appearance of these wounds may provide meaningful clues that may help physicians in the diagnosis. In this context, we propose ICARUS, an image retrieval system for dermatol...Show More
Computed Tomography (CT) scans are often employed to diagnose lung diseases, as abnormal tissue regions may indicate whether proper treatment is required. However, detecting specific regions containing abnormalities in a CT scan demands time and effort of specialists. Moreover, different parts of a single lung image may present both normal and abnormal characteristics, what makes inaccurate the cl...Show More
Performing content-based image retrieval over large repositories of medical images demands efficient computational techniques. The use of such techniques is intended to speed up the work of physicians, who often have to deal with information from multiple data repositories. When dealing with multiple data repositories, the common computational approach is to search each repository separately and m...Show More
In social voting Web sites, how do the user actions - up-votes, down-votes and comments - evolve over time? Are there relationships between votes and comments? What is normal and what is suspicious? These are the questions we focus on. We analyzed over 20,000 submissions corresponding to more than 100 million user interactions from three social voting Web sites: Reddit, Imgur and Digg. Our first c...Show More
Crowdsourcing images have been increasingly employed for mapping emergency scenarios, which helps rescue forces in choosing contingency plans. In this scenario, similarity searching can be used to retrieve related images from past situations. However, the retrieved images often are similar among themselves and, therefore, add little to none new information to the rescue decision-making process. In...Show More