Encoding Deep Residual Features into Fisher Vector for Skin Lesion Classification.

Computer-aided skin lesion classification using dermoscopy is essential for early detection of melanoma, which is the most effective means to reduce the mortality rate. Although many deep learning models have been designed for this task, skin lesion classification remains challenging due to the small sample size, inter-class similarity, intra-class inconsistency, and class imbalance. In this paper, we propose a hybrid deep residual network and Fisher vector (ResNet-FV) algorithm for skin lesion classification, aiming to boost the performances of ResNet using the Fisher vector encoding scheme. The proposed algorithm has been evaluated on the 2018 Skin Lesion Analysis Towards Melanoma Detection Challenge (ISIC-skin 2018) dataset and achieved a balanced multi-class accuracy of 0.798, outperforming several existing solutions. Clinical relevance- We propose a computer-aided diagnosis algorithm called ResNet-FV which achieves superior performance when comparing to several existing solutions and hence has the potential to be applied to large-scale skin cancer screening.

Retinal Vessel Segmentation Based on B-COSFIRE Filters in Fundus Images.

Retinal vessel extraction plays an important role in the diagnosis of several medical pathologies, such as diabetic retinopathy and glaucoma. In this article, we propose an efficient method based on a B-COSFIRE filter to tackle two challenging problems in fundus vessel segmentation: (i) difficulties in improving segmentation performance and time efficiency together and (ii) difficulties in distinguishing the thin vessel from the vessel-like noise. In the proposed method, first, we used contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement, then excerpted region of interest (ROI) by thresholding the luminosity plane of the CIELab version of the original RGB image. We employed a set of B-COSFIRE filters to detect vessels and morphological filters to remove noise. Binary thresholding was used for vessel segmentation. Finally, a post-processing method based on connected domains was used to eliminate unconnected non-vessel pixels and to obtain the final vessel image. Based on the binary vessel map obtained, we attempt to evaluate the performance of the proposed algorithm on three publicly available databases (DRIVE, STARE, and CHASEDB1) of manually labeled images. The proposed method requires little processing time (around 12 s for each image) and results in the average accuracy, sensitivity, and specificity of 0.9604, 0.7339, and 0.9847 for the DRIVE database, and 0.9558, 0.8003, and 0.9705 for the STARE database, respectively. The results demonstrate that the proposed method has potential for use in computer-aided diagnosis.