The extended utilization of digitized Whole Slide Images is transforming the workflow of traditional clinical histopathology to the digital era. The ongoing transformation has demonstrated major potentials towards the exploitation of Machine Learning and Deep Learning techniques as assistive tools for specialized medical personnel. While the performance of the implemented algorithms is continually boosted by the mass production of generated Whole Slide Images and the development of state-of the-art deep convolutional architectures, ensemble models provide an additional methodology towards the improvement of the prediction accuracy. Despite the earlier belief related to deep convolutional networks being treated as black boxes, important steps for the interpretation of such predictive models have also been proposed recently. However, this trend is not fully unveiled for the ensemble models. The paper investigates the application of an explanation scheme for ensemble classifiers, while providing satisfactory classification results of histopathology breast and colon cancer images in terms of accuracy. The results can be interpreted by the hidden layers’ activation of the included subnetworks and provide more accurate results than single network implementations.

Vitiligo, alopecia areata, atopic, and stasis dermatitis are common skin conditions that pose diagnostic and assessment challenges. Skin image analysis is a promising noninvasive approach for objective and automated detection as well as quantitative assessment of skin diseases. This review provides a systematic literature search regarding the analysis of computer vision techniques applied to these benign skin conditions, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The review examines deep learning architectures and image processing algorithms for segmentation, feature extraction, and classification tasks employed for disease detection. It also focuses on practical applications, emphasizing quantitative disease assessment, and the performance of various computer vision approaches for each condition while highlighting their strengths and limitations. Finally, the review denotes the need for disease-specific datasets with curated annotations and suggests future directions toward unsupervised or self-supervised approaches. Additionally, the findings underscore the importance of developing accurate, automated tools for disease severity score calculation to improve ML-based monitoring and diagnosis in dermatology. Not applicable.

Along with the increase in the accuracy of artificial intelligence systems, complexity has also risen. Despite high accuracy, high-risk decision-making requires explanations about the model’s decision, which often take the form of saliency maps. This work examines the efficacy of ensembling deep convolutional neural networks to leverage explanations, under the concept that ensemble models are combinatory informed. A novel approach is presented for aggregating saliency maps derived from multiple base models, as an alternative way of combining the different perspectives that several competent models offer. The proposed methodology lowers computation costs, while allowing for the combinations of maps of various origins. Following a saliency map evaluation scheme, four tests are performed over three image datasets, two medical image datasets and one generic. The results suggest that interpretability is improved by combining information through the aggregation scheme. The discussion that follows provides insights into the inner workings behind the results, such as the specific combination of the interpretability and ensemble methods, and offers useful suggestions for future work.