Daniel Yoshinobu Takada Chino

Bedridden patients with skin lesions (ulcers) often do not have access to specialized clinic equipment. It is important to allow healthcare practitioners to use their smartphones to leverage information regarding the proper treatment to be carried. Existing applications require special equipment, such as heat sensors, or focus only on general information. To fulfill this gap, we propose ULEARn, a DBMS-based framework for the processing of ulcer images, providing tools to store and retrieve similar images of past cases. The proposed mobile application ULEARn-App allows healthcare practitioners to send a photo from a patient to ULEARn, and obtain a timely feedback that allows the improvement of procedures on therapeutic interventions. Experimental results of ULEARn and ULEARn-App using a real-world dataset showed that our tool can quickly respond to the required analysis and retrieval tasks, being up to 4.6 times faster than the specialist’ expected execution time.

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 dermatological ulcer images based on Bag-of-Visual-Words of color and texture signatures. ICARUS analyzes the image and extracts only the relevant signatures. The results show that ICARUS achieves improvement of up to 7% in image retrieval precision whereas being up to 5 orders of magnitude faster when compared to the state-of-the-art methods. Our results showed that ICARUS is effective and fast, and successfully adds semantic to the image representation.

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 physicians with intelligent retrieval tools to present him/her with similar or complementary cases, in which visual characteristics improve textual data. Employing comparative inspection on previous cases, the physician can obtain a more comprehensive understanding of the case he/she is working on. Current hospital systems do not carry native CBIR functionalities yet, relying on add-on subsystems, which often do not adhere to the existing relational database infrastructures. In this work, we propose RAFIKI, a software prototype that extends the Relational Database Management System (RDBMS) PostgreSQL, providing native support for CBIR functionalities, modular extensibility, and seamless integration for data science tools, such as Python and R. We show the applicability of our system by evaluating three clinical scenarios, performing queries over a real-world image dataset of lung exams. Our results spot actual potential in promoting informed decision-making from the physician's perspective. Besides, the system exhibited a higher performance when compared to previous systems found in the literature. Moreover, RAFIKI contributes with a model to establish how to put together CBIR concepts and relational data, providing a powerful design for further development of theoretical and practical concepts and tools.

Is it possible to spot review frauds and spamming on social media and online stores? In this paper we analyze the joint distribution of the inter-arrival times and volume of events such as comments and online reviews and show that it is possible to accurately rank and detect suspicious users such as spammers, bots and fraudsters. We propose VolTime, a generative model that fits well the inter-arrival time distribution (IAT) of real users. Thus, VOLTIME automatically spots and ranks suspicious users. Experiments on several real datasets, ranging from Reddit comments and phone calls to Flipkart product reviews, show that VolTime is able to accurately fit the activity volume and IAT of real data. Additionally, we show that VolTime ranks suspicious users with a precision higher than 90% for a sensitivity of 70%.

O crescente aumento no volume de dados complexos tem se tornado um desafio para pesquisadores. Séries temporais são um tipo de dados complexos que tem tido um crescimento em sua relevância, devido a sua...

Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowd sourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on super pixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.

Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowdsourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on superpixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.