Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.

Stromal Myofibroblasts In Squamous Cell Carcinoma Of The Tongue In Young Patients - A Multicenter Collaborative Study.

The purpose of this study was to evaluate the presence of myofibroblasts, frequently associated with a more aggressive neoplastic behavior, in oral tongue squamous cell carcinoma (TSCC) of young patients and to compare with the distribution observed in older patients. Tumor samples from 29 patients younger than 40 years old affected by TSCC were retrieved and investigated for the presence of stromal myofibroblasts by immunohistochemical reactions against α smooth muscle actin, and the results obtained were compared to TSCC cases affecting older patients. No positive reaction could be found in the stromal areas devoid of neoplastic tissue, whereas myofibroblasts were present in 58.6% of the lesions in young patients and in 75.9% of the older ones. No significant difference was found when comparing the invasive front and the overall stroma of both groups, and no correlation could be obtained with stromal α smooth muscle actin expression, higher tumor grades or clinical stage (P > .05). There was no significant difference between the presence of stromal myofibroblasts of TSCC affecting young and old individuals.