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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.

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.

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.

This paper presents a framework that optimizes kernel-based manifold embedding for the characterization of multispectral image data. The hypothesis is that data manifolds corresponding to high-dimensional images can have varying characteristics and types of nonlinearity. As a result, kernel functions must be selected from a wide range of transformations and tuned on an image- and patient-basis. To this end, we introduce a new measure to assess the quality of the kernel transformations that takes into account both local and global relationships in nonlinear manifolds. Furthermore, the calculated measures for each kernel are used to combine the different kernel transformations further highlight the tissue constituents in all regions of the image. Validation with phantom and real multispectral image data shows improvement in the visualization and characterization of the tissue constituents.

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.