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.

Transition From Glass To Digital Slide Microscopy In The Teaching Of Oral Pathology In A Brazilian Dental School.

Several medical and dental schools have described their experience in the transition from conventional to digital microscopy in the teaching of general pathology and histology disciplines; however, this transitional process has scarcely been reported in the teaching of oral pathology. Therefore, the objective of the current study is to report the transition from conventional glass slide to virtual microscopy in oral pathology teaching, a unique experience in Latin America. An Aperio ScanScope® scanner was used to digitalize histological slides used in practical lectures of oral pathology. The challenges and benefits observed by the group of Professors from the Piracicaba Dental School (Brazil) are described and a questionnaire to evaluate the students' compliance to this new methodology was applied. An improvement in the classes was described by the Professors who mainly dealt with questions related to pathological changes instead of technical problems; also, a higher interaction with the students was described. The simplicity of the software used and the high quality of the virtual slides, requiring a smaller time to identify microscopic structures, were considered important for a better teaching process. Virtual microscopy used to teach oral pathology represents a useful educational methodology, with an excellent compliance of the dental students.