Overview of ImageCLEF 2017: Information extraction from images

This paper presents an overview of the ImageCLEF 2017 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) labs 2017. ImageCLEF is an ongoing initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval for providing information access to collections of images in various usage scenarios and domains. In 2017, the 15th edition of ImageCLEF, three main tasks were proposed and one pilot task: (1) a LifeLog task about searching in LifeLog data, so videos, images and other sources; (2) a caption prediction task that aims at predicting the caption of a figure from the biomedical literature based on the figure alone; (3) a tuberculosis task that aims at detecting the tuberculosis type from CT (Computed Tomography) volumes of the lung and also the drug resistance of the tuberculosis; and (4) a remote sensing pilot task that aims at predicting population density based on satellite images. The strong participation of over 150 research groups registering for the four tasks and 27 groups submitting results shows the interest in this benchmarking campaign despite the fact that all four tasks were new and had to create their own community

A graph model of the lungs with morphology-based structure for tuberculosis type classification

Pulmonary tuberculosis (TB) is still an important cause of death worldwide, even after being almost eradicated 40 years ago. Early identification of TB in computed tomography (CT) scans can influence therapeutical decisions, thus improving patient outcome. In this paper, a graph model of the lungs is proposed for the classification of TB types using local texture features in thorax CT scans of TB patients. Based on lung morphology, an automatic patient-specific lung field parcellation was initially computed. Local visual features were then extracted from each region and were used to build a personalized lung graph model. A new graph-based patient descriptor enables comparisons between lung graphs with a different number of nodes and edges, encoding the distribution of several node measures from graph theory. The proposed model was trained and tested on a public dataset of 1,513 CT scans of 994 TB patients. The evaluation was performed on data from a scientific challenge together with 39 participant algorithms, obtaining the best unweighted Cohen kappa coefficient of 0.24 in a 5-class setup with 505 test CTs. Even though each lung graph has a unique structure, the proposed method was able to identify key texture changes associated with the different manifestations of TB

From Local to Global: A Holistic Lung Graph Model

Lung image analysis is an essential part in the assessment of pulmonary diseases. Through visual inspection of CT scans, radiologists detect abnormal patterns in the lung parenchyma, aiming to establish a timely diagnosis and thus improving patient outcome. However, in a generalized disorder of the lungs, such as pulmonary hypertension, the changes in organ tissue can be elusive, requiring additional invasive studies to confirm the diagnosis. We present a graph model that quantifies lung texture in a holistic approach enhancing the analysis between pathologies with similar local changes. The approach extracts local state-of-the-art 3D texture descriptors from an automatically generated geometric parcellation of the lungs. The global texture distribution is encoded in a weighted graph that characterizes the correlations among neighboring organ regions. A data set of 125 patients with suspicion of having a pulmonary vascular pathology was used to evaluate our method. Three classes containing 47 pulmonary hypertension, 31 pulmonary embolism and 47 control cases were classified in a one vs. one setup. An area under the curve of up to 0.85 was obtained adding directionality to the edges of the graph architecture. The approach was able to identify diseased patients, and to distinguish pathologies with abnormal local and global blood perfusion defects

Textured graph-based model of the lungs ::application on tuberculosis type classification and multi-drug resistance detection

Tuberculosis (TB) remains a leading cause of death worldwide. Two main challenges when assessing computed tomography scans of TB patients are detecting multi{drug resistance and di_erentiating TB types. In this article we model the lungs as a graph entity where nodes represent anatomical lung regions and edges encode interactions between them. This graph is able to characterize the texture distribution along the lungs, making it suitable for describing patients with different TB types. In 2017, the ImageCLEF benchmark proposed a task based on computed tomography volumes of patients with TB. This task was divided into two subtasks: multi{drug resistance prediction, and TB type classi_cation. The participation in this task showed the strength of our model, leading to best results in the competition for multi{drug resistance detection (AUC = 0.5825) and good results in the TB type classi_cation (Cohen's Kappa coe_cient = 0.1623)

Real-time multi-agent systems: rationality, formal model, and empirical results

Since its dawn as a discipline, Artificial Intelligence (AI) has focused on mimicking the human mental processes. As AI applications matured, the interest for employing them into real-world complex systems (i.e., coupling AI with Cyber-Physical Systems—CPS) kept increasing. In the last decades, the multi-agent systems (MAS) paradigm has been among the most relevant approaches fostering the development of intelligent systems. In numerous scenarios, MAS boosted distributed autonomous reasoning and behaviors. However, many real-world applications (e.g., CPS) demand the respect of strict timing constraints. Unfortunately, current AI/MAS theories and applications only reason “about time” and are incapable of acting “in time” guaranteeing any timing predictability. This paper analyzes the MAS compliance with strict timing constraints (real-time compliance)—crucial for safety-critical applications such as healthcare, industry 4.0, and automotive. Moreover, it elicits the main reasons for the lack of real-time satisfiability in MAS (originated from current theories, standards, and implementations). In particular, traditional internal agent schedulers (general-purpose-like), communication middlewares, and negotiation protocols have been identified as co-factors inhibiting real-time compliance. To pave the road towards reliable and predictable MAS, this paper postulates a formal definition and mathematical model of real-time multi-agent systems (RT-MAS). Furthermore, this paper presents the results obtained by testing the dynamics characterizing the RT-MAS model within the simulator MAXIM-GPRT. Thus, it has been possible to analyze the deadline miss ratio between the algorithms employed in the most popular frameworks and the proposed ones. Finally, discussing the obtained results, the ongoing and future steps are outlined

Rotation Invariance and Directional Sensitivity: Spherical Harmonics versus Radiomics Features

We define and investigate the Local Rotation Invariance (LRI) and Directional Sensitivity (DS) of radiomics features. Most of the classical features cannot combine the two properties, which are antagonist in simple designs. We propose texture operators based on spherical harmonic wavelets (SHW) invariants and show that they are both LRI and DS. An experimental comparison of SHW and popular radiomics operators for classifying 3D textures reveals the importance of combining the two properties for optimal pattern characterization