Abstract
Gastroenterology imaging is an essential tool to detect gastrointestinal cancer in patients. Computer-assisted diagnosis is desirable to help us improve the reliability of this detection. However, traditional computer vision methodologies, mainly segmentation, do not translate well to the specific visual characteristics of a gastroenterology imaging scenario. In this paper, we propose a novel method for the segmentation of gastroenterology images from two distinct imaging modalities and organs: chromoendoscopy (CH) and narrow-band imaging (NBI) from stomach and esophagus, respectively. We have used various visual features individually and their combinations (edgemaps, creaseness, and color) in normalized cuts image segmentation framework to segment ground truth datasets of 142 CH and 224 NBI images. Experiments show that an integration of edgemaps and creaseness in normalized cuts gives the best segmentation performance resulting in high-quality segmentations of the gastroenterology images.

Abstract
When mammography reveals a suspicious finding, a core needle biopsy is usually recommended. In 5% to 15% of these cases, the biopsy diagnosis is non-definitive and a more invasive surgical excisional biopsy is recommended to confirm a diagnosis. The majority of these cases will ultimately be proven benign. The use of excisional biopsy for diagnosis negatively impacts patient quality of life and increases costs to the healthcare system. In this work, we employ a multi-relational machine learning approach to predict when a patient with a non-definitive core needle biopsy diagnosis need not undergo an excisional biopsy procedure because the risk of malignancy is low. © 2013 IEEE.

Abstract
The present context of women physicists in Portugal is discussed, updating our report for the 2002 IUPAP International Conference on Women in Physics, in which the 30 years prior to 2000 were analyzed.

Abstract

Abstract
The success of artificial prosthetic replacements depends on the fixation of the artificial prosthetic component after being implanted in the thighbone. This work shows a smart prosthesis based on highly sensitive silicon thin-film piezoresistive sensors attached to a hip prosthesis. The performance of the sensors for this application is studied and compared to commercial strain gauge sensors. Mechanical stress-strain experiments were performed in compressive mode, during 10,000 cycles and data was acquired at mechanical vibration frequencies of 0.5 Hz, I Hz and 5 Hz, and sent to a computer by means of a wireless link. The results show that there is a decrease in sensitivity of the thin-film silicon piezoresistive (n-type nanocrystalline Si) sensors when they are attached to the prosthesis, however this decrease does not compromise its monitoring performance. The sensitivity, compared to that of commercial strain gauges, is much larger due to their higher gauge factor (-23.5), when compared to the gage factor of commercial sensors (2).

Abstract
Falls of patients are an important issue in hospitals nowadays; it causes severe injuries, increases hospitalization time and treatment costs. The detection of a fall, in time, provides faster rescue to the patient, preventing more serious injuries, as well as saving nursing time. The MovinSense (R) is an electronic device designed for monitoring patients to prevent pressure sores, and the main goal of this work was to develop a new tool for this device, with the purpose of detecting if the patient has fallen from the hospital bed, without changing any of the device's original features. Experiments for gathering data samples of inertial signals of falling from the bed were obtained using the device. For fall detection a sensitivity of 72% and specificity of 100% were reached. Another algorithm was developed to detect if the patient got out of his/her bed.