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Colorectal polyps are an indicator of colorectal cancer (CRC). Classification of polyps during colonoscopy is still a challenge for which many medical experts have come up with visual models albeit with limited success. In this paper, a classification approach is proposed to differentiate between polyp malignancy, using features extracted from the Global Average Pooling (GAP) layer of a Convolutional Neural Network (CNNs). Two recent endoscopic modalities are used to improve the algorithm prediction: Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). Furthermore, a new strategy of per-class data augmentation is adopted to tackle an unbalanced class distribution and to improve the decision of the classifiers. As a result, we increase the performance compared to state-of-the-art methods (0.97 vs 0.90 AUC). Our method for automatic polyp malignancy classification facilitates future advances towards patient safety and may avoid time-consuming and costly histopathological assessment.

The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken into account in clinical routine Dual-energy X-ray Absorptiometry (DXA) examination. In this paper, a statistical shape and appearance model together with a 3D-2D registration approach are used to model the femoral shape and bone density distribution in 3D from an anteroposterior DXA projection. A model-based algorithm is subsequently used to segment the cortex and build a 3D map of the cortical thickness and density. Measurements characterising the geometry and density distribution were computed for various regions of interest in both cortical and trabecular compartments. Models and measurements provided by the "3D-DXA" software algorithm were evaluated using a database of 157 study subjects, by comparing 3D-DXA analyses (using DXA scanners from three manufacturers) with measurements performed by Quantitative Computed Tomography (QCT). The mean point-to-surface distance between 3D-DXA and QCT femoral shapes was 0.93 mm. The mean absolute error between cortical thickness and density estimates measured by 3D-DXA and QCT was 0.33 mm and 72mg/cm(3). Correlation coefficients (R) between the 3D-DXA and QCT measurements were 0.86, 0.93, and 0.95 for the volumetric bone mineral density at the trabecular, cortical, and integral compartments respectively, and 0.91 for the mean cortical thickness. 3D-DXA provides a detailed analysis of the proximal femur, including a separate assessment of the cortical layer and trabecular macrostructure, which could potentially improve osteoporosis management while maintaining DXA as the standard routine modality.

Given the lack of relevant data, the aim of this study was to examine femur cortical and trabecular bone in female and male professional ballet dancers. 40 professional ballet dancers and 40 sex- and age-matched non-exercising controls volunteered. Femoral bone density was scanned by dual-energy X-ray absorptiometry (DXA) scan. A 3D-DXA software was used to analyse trabecular and cortical bone. Anthropometry, maturation (Tanner staging), menstrual parameters (age at menarche and primary amenorrhea), energy availability and nutritional analysis (3-day record) were also assessed.Compared to non-exercising participants, dancers exhibited significantly higher volumetric density for integral, cortical and trabecular bone, and thicker cortex at the femur. Ballet dancers demonstrated lower body weight compared to controls (p 0.01). Female dancers had their menarche later than controls, and the prevalence of primary amenorrhea were significantly higher in dancers than controls (p 0.01). Dancer's energy availability was below the normal range (30 kcal/kgFFM/day). Despite the presence of certain osteoporosis risk factors such as low energy availability, primary amenorrhoea and lower body weight, professional ballet dancers revealed higher bone density for both cortical and trabecular bone compartments compared to controls.

In-vivo classification of benign and pre-malignant polyps is a laborious task that requires histophatology confirmation. In an effort to improve the quality of clinical diagnosis, medical experts have come up with visual models with only limited success. In this paper, a classification approach is proposed to differentiate between polypmalignancy, using features extracted from the Global Average Pooling (GAP) layer of a pre-trained Convolutional Neural Network (CNNs) . Two recently developed endoscopic modalities are used to improve the pipeline prediction: Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). Furthermore, a new strategy of per-class data augmentation is adopted to tackle the differences of unbalanced class distribution. The results are compared with a more general approach, showing how artificial examples can improve results on highly unbalanced problems. For the same reason, the combined features for each patient are extracted and trained using several machine learning classifiers without CNNs. Moreover to speed up computation, a recent GPU based Support Vector Machine (SVM) scheme is employed to substantially decrease the overload during training time. The presented methodology shows the feasibility of using the LCI and BLI techniques for automatic polypmalignancy classification and facilitates future advances to limit the need for time-consuming and costly histopathological assessment.

High bone mass (HBM), a rare phenotype, can be detected by dual-energy X-ray absorptiometry (DXA) scanning. Measurements with peripheral quantitative computed tomography at the tibia have found increased trabecular bone mineral density and changes in cortical bone density and structure, all of which lead to increased bone strength. However, no studies on cortical and trabecular bone have been performed at the femur. The recently developed 3-dimensional (3D)-DXA software algorithm quantifies the trabecular and cortical volumetric bone mineral density (vBMD) and the anatomical distribution of cortical thickness using routine hip DXA scans. We analyzed the femurs of 15 women with HBM and 15 controls from the Barcelona Osteoporosis (BARCOS) cohort using the 3D-DXA technique. The mean vBMD of proximal femur was 29.7% higher in HBM cases than in controls for the integral bone, 41.3% higher for the trabecular bone, and 7.3% higher for the cortical bone (p < 0.001). No differences in bone size were detected between cases and controls. Patients with HBM had a thicker cortex and higher trabecular and cortical vBMDs, as measured by 3D-DXA at the femur and compared to controls; bone size was similar in both groups. To the best of our knowledge, this is the first description of trabecular and cortical characteristics of the hip in patients with HBM.

The distractive effects on cognitive processes ascribed to the nature of sound havebeen studied in the paradigm of ”irrelevant sound,” where test participants perform cognitivetasks in the presence of background noise. By comparing the test scores for different acousticstimulus conditions in such experiments, the ”irrelevant sound (speech) effect” (ISE) can bequantified. The ISE is often explained by the changing state hypothesis: the distinctivesegmentation of sound tokens; where tokens may be understood as sound segments that canbe distinguished from each other in temporal and/or spectral characteristics. A sequence ofsounds consisting of differing tokens produces much more disruption than a steady-statesound. The present work investigates the relationship between the features from bothtemporal and spectral domains and the ISE, predicting separately the magnitude of the effectwith two estimators: The Average Modulation Transfer Function (AMTF) and the FrequencyDomain Correlation Coefficient (FDCC). The first parameter is a measure for temporalvariations in a sound, whilst the latter measures spectral variability in the sounds. Backgroundstimuli are synthesized from a pulse train in which modified and unmodified pulses alternate.In order to manipulate the temporal and spectral features in the stimuli, a numericaloptimization method was used to generate two sets of background stimuli where one of thetwo descriptors was always kept constant and the other was varied in a systematic way.Therefore, stimulus sets used in this study allow the separate estimation of the role of the twoestimators on cognitive performance in tasks involving serial ordering of short-term memorycontent.

The main curative treatment for localized colon cancer is surgical resection. However when tumor residuals are left positive margins are found during the histological examinations and additional treatment is needed to inhibit recurrence. Hyperspectral imaging (HSI) can offer non-invasive surgical guidance with the potential of optimizing the surgical effectiveness. In this paper we investigate the capability of HSI for automated colon cancer detection in six ex-vivo specimens employing a spectral-spatial patch-based classification approach. The results demonstrate the feasibility in assessing the benign and malignant boundaries of the lesion with a sensitivity of 0.88 and specificity of 0.78. The results are compared with the state-of-the-art deep learning based approaches. The method with a new hybrid CNN outperforms the state-of the-art approaches (0.74 vs. 0.82 AUC). This study paves the way for further investigation towards improving surgical outcomes with HSI.