Enriched dermoscopic-structure-based cad system for melanoma diagnosis

Abstract

Computer-Aided Diagnosis (CAD) systems for melanoma detection have received a lot of attention during the last decades because of the utmost importance of detecting this type of skin cancer in its early stages. However, despite of the many research efforts devoted to this matter, these systems are not used yet in everyday clinical practice. Very likely, this is due to two main reasons: 1) the accuracy of the systems is not high enough; and 2) they simply provide a parallel diagnosis that actually does not help to the doctors (as long as there is no way to interpret it).In this paper, we propose a novel approach that aims to provide the doctor with an enriched diagnosis. Specifically, we rely on a dermoscopic-structurebased soft segmentation to design a set of structure-specific classifiers. Each individual structure-specific classifier is trained to distinguish benign lesions from melanomas just paying attention to one type of dermoscopic structure. Then, the outputs of the individual classifiers are combined by a means of the Bayesian method that, besides the final diagnosis, provide the doctor with additional valuable information, such as the opinions of the individual structurespecific experts and the uncertainty of the diagnosis.The results in terms of the features selected for the structure-specific classifiers are consistent with the expert insights. Furthermore, regarding the auto-

Probabilistic Topic Model for Context-Driven Visual Attention Understanding

“…Some examples of ML tasks are classification, regression and density estimation. Tasks in computer vision such as image classification, retrieval and segmentation have been tackled both in supervised [36][37][38] or unsupervised [39,40] experiences. The paramount tasks we aim to solve in this thesis are spatio-temporal and temporal visual attention modeling: first, we interpret how spatio-temporal visual attention works in several contexts, and then we apply it in a video surveillance scenario for temporal modeling of attention.…”

Multidirectional Analysis of Curvelet Against Skin Cancer

“…In particular, the mechanisms are able to recognize the pattern in the dermoscopic images based on the extracted features. When a medical image or signal is analyzed by a CAD system, any anomalies from the typical patterns are detected [36][37][38][39][40]. In general, the underlying premise of the Computer Aided Diagnosis (CAD) system is that nearby is a geometric difference among usual besides unusual images.…”

Dermo-Seg: ResNet-UNet Architecture and Hybrid Loss Function for Detection of Differential Patterns to Diagnose Pigmented Skin Lesions

“…Consequently, a computer-aided diagnostic (CAD) system is necessary to help dermatologists in analyzing these attributes. Numerous diagnostic techniques, including CAD systems, have been proposed in the literature for skin lesion segmentation [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. However, many of these techniques primarily focus on boundary segmentation [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ,…”

“…Kawahara J et al [ 36 ] reframe the objective of categorizing clinical dermoscopic attributes within super-pixels by handling it as a segmentation challenge. They put forth a fully convolutional neural network that is specifically designed to identify these dermoscopic attributes in dermoscopy images.…”

“…Numerous diagnostic techniques, including CAD systems, have been proposed in the literature for skin lesion segmentation . However, many of these techniques primarily focus on boundary segmentation while giving less attention to the segmentation of actual attributes within the lesion [29][30][31][32][33][34][35][36][37][38][39][40][41][42]. The purpose of employing CAD systems is to automatically identify irregularities or skin disorders present within the lesion.…”