The early detection of melanoma is the most efficient way to reduce its mortality rate. Dermatologists achieve this task with the help of dermoscopy, a non-invasive tool allowing the visualization of patterns of skin lesions. Computer-aided diagnosis (CAD) systems developed on dermoscopic images are needed to assist dermatologists. These systems rely mainly on multiclass classification approaches. However, the multiclass classification of skin lesions by an automated system remains a challenging task. Decomposing a multiclass problem into a binary problem can reduce the complexity of the initial problem and increase the overall performance. This paper proposes a CAD system to classify dermoscopic images into three diagnosis classes: melanoma, nevi, and seborrheic keratosis. We introduce a novel ensemble scheme of convolutional neural networks (CNNs), inspired by decomposition and ensemble methods, to improve the performance of the CAD system. Unlike conventional ensemble methods, we use a directed acyclic graph to aggregate binary CNNs for the melanoma detection task. On the ISIC 2018 public dataset, our method achieves the best balanced accuracy (76.6%) among multiclass CNNs, an ensemble of multiclass CNNs with classical aggregation methods, and other related works. Our results reveal that the directed acyclic graph is a meaningful approach to develop a reliable and robust automated diagnosis system for the multiclass classification of dermoscopic images.

Early detection of melanoma remains a daily challenge due to the increasing number of cases and the lack of dermatologists. Thus, AI-assisted diagnosis is considered as a possible solution for this issue. Despite the great advances brought by deep learning and especially convolutional neural networks (CNNs), computer-aided diagnosis (CAD) systems are still not used in clinical practice. This may be explained by the dermatologist’s fear of being misled by a false negative and the assimilation of CNNs to a “black box”, making their decision process difficult to understand by a non-expert. Decision theory, especially game theory, is a potential solution as it focuses on identifying the best decision option that maximizes the decision-maker’s expected utility. This study presents a new framework for automated melanoma diagnosis. Pursuing the goal of improving the performance of existing systems, our approach also attempts to bring more transparency in the decision process. The proposed framework includes a multi-class CNN and six binary CNNs assimilated to players. The players’ strategies is to first cluster the pigmented lesions (melanoma, nevus, and benign keratosis), using the introduced method of evaluating the confidence of the predictions, into confidence level (confident, medium, uncertain). Then, a subset of players has the strategy to refine the diagnosis for difficult lesions with medium and uncertain prediction. We used EfficientNetB5 as the backbone of our networks and evaluated our approach on the public ISIC dataset consisting of 8917 lesions: melanoma (1113), nevi (6705) and benign keratosis (1099). The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.93 for melanoma, 0.96 for nevus and 0.97 for benign keratosis. Furthermore, our approach outperformed existing methods in this task, improving the balanced accuracy (BACC) of the best compared method from 77% to 86%. These results suggest that our framework provides an effective and explainable decision-making strategy. This approach could help dermatologists in their clinical practice for patients with atypical and difficult-to-diagnose pigmented lesions. We also believe that our system could serve as a didactic tool for less experienced dermatologists.

This article describes the set of photogrammetric tools developed for the monitoring of Mediterranean red coral Corallium rubrum populations. The description encompasses the full processing chain: from the image acquisition to the information extraction and data interpretation. The methods applied take advantage of existing tools and new, innovative and specific developments in order to acquire data on relevant ecological information concerning the structure and functioning of a red coral population. The tools presented here are based on: (i) automatic orientation using coded quadrats; (ii) use of non-photorealistic rendering (NPR) and 3D skeletonization techniques; (iii) computation of distances between colonies from a same site; and (iv) the use of a plenoptic approach in an underwater environment.

Melanoma is still a major health problem worldwide. The early diagnosis is the first step toward reducing its mortality, but it remains a challenge even for experienced dermatologists. While computer-aided systems have been developed to help the diagnosis, the lack of insight of their predictions is still a significant limitation towards acceptance by the medical community. To tackle this issue, we designed handcrafted expert features representing color asymmetry within the lesions, which are parts of the approach used by dermatologists in their daily practice. These features are given to an artificial neural network (ANN) classifying between nevi and melanoma. We compare our results with an ensemble of seven state-of-the-art CNNs and merge the two approaches by computing the average prediction. Our experiments are done on a subset of the International Skin Imaging Collaboration (ISIC) 2019 dataset (6296 nevi, 1361 melanomas). The ANN based on asymmetry achieved an AUC of 0.873, Sensitivity 90%, Specificity 67%, the CNN approach achieved an AUC of 0.938, Sensitivity 91%, Specificity 82%, and the fusion of both approaches was 0.942, Sensitivity 92%, Specificity 82%. Merging the knowledge of dermatologists with CNNs showed high performances for melanoma detection, encouraging collaboration between computer science and medical fields.

This paper provides details of hardware and software conception and realization of a stereo embedded system for underwater imaging. The system provides several functions that facilitate underwater surveys and run smoothly in real-time. A first post-image acquisition module provides direct visual feedback on the quality of the taken images which helps appropriate actions to be taken regarding movement speed and lighting conditions. Our main contribution is a light visual odometry method adapted to the underwater context. The proposed method uses the captured stereo image stream to provide real-time navigation and a site coverage map which is necessary to conduct a complete underwater survey. The visual odometry uses a stochastic pose representation and semi-global optimization approach to handle large sites and provides long-term autonomy, whereas a novel stereo matching approach adapted to underwater imaging and system attached lighting allows fast processing and suitability to low computational resource systems. The system is tested in a real context and shows its robustness and promising future potential.

Due to its increasing incidence, skin cancer, and especially melanoma, is considered a major public health issue. Manually detecting skin lesions (SL) from dermoscopy images is a difficult and time-consuming process. Thus, researchers designed computer-aided diagnosis (CAD) systems to assist dermatologists in the early detection of skin cancer. Moreover, SL detection naturally exhibits a long-tailed distribution due to the complex patient-level conditions and the existence of rare diseases. Very limited research for handling this issue exists on SL detection. In this paper, we propose an end-to-end decoupled training for the long-tailed skin lesion classification task. Specifically, we initialized the training of a network with a novel loss function $Lf$ able to guide the model to a better representation of the features. Then, we fine-tuned the pretrained networks with a weighted variant of $Lf$ helping to improve the robustness of the network to class imbalance. We evaluated our model on the ISIC 2018 public dataset against existing methods for handling class imbalance and existing approaches for SL detection. The results demonstrated the superiority of our framework, outperforming all compared methods by a minimum margin of 2% with a single model.