The paper presents and comparatively analyses several deep learning approaches to automatically detect tuberculosis related lesions in lung CTs, in the context of the ImageClef 2020 Tuberculosis task.
Advanced stage nasopharyngeal cancer (NPC) shows highly variable treatment outcomes, suggesting the need for independent prognostic factors.
In this work, we present a robotic storytelling system, where the characters have been modelled as cognitive agents embodied in Pepper and NAO robots.
The purpose of this study was to establish a methodology and technology for the development of an MRI-based radiomic signature for prognosis of overall survival (OS) in nasopharyngeal cancer from non-endemic areas. The signature was trained using 1072 features extracted from the main tumor in T1-weighted and T2-weighted images of 142 patients.
Knowledge about the expected interaction duration and expected distance from which users will interact with public displays can be useful in many ways.
Postural instability is one of the most troublesome motor symptoms of Parkinson’s Disease (PD). It impairs patients’ quality of life and results in high risk of falls.
Emerging data suggest that radiomics can be used to predict outcomes in SCCHN. At present, only few data are available for pre-treatment MRI.
Prognosis of extremity soft tissue sarcomas (ESTS) and retroperitoneal sarcomas (RPS) is currently estimated on clinical-pathological features, as those incorporated in the Sarculator nomogram.
The rupture of a vulnerable plaque, known as ulceration, is the most common cause of myocardial infarction. It can be recognized by angiographic features, such as prolonged intraluminal filling and delayed clearance of the contrast liquid. The diagnosis of such an event is an open challenge due to the limited angiographic resolution and acquisition frequency.
In this paper, several radiomics-based predictive models of response to induction chemotherapy (IC) in sinonasal cancers (SNCs) are built and tested. Models were built as a combination of radiomic features extracted from three types of MRI images: T1-weighted images, T2-weighted images and apparent diffusion coefficient (ADC) maps.