Pazilova Nasibaxon Muhammadqosimovna, Rasulov Fayzulla

TL;DR: This study surveyed the current progress of XAI and in particular its advances in healthcare applications, and introduced the solutions for XAI leveraging multi-modal and multi-centre data fusion, and subsequently validated in two showcases following real clinical scenarios.

TL;DR: Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made as mentioned in this paper , which is particularly true of the most popular deep neural network approaches currently in use.

TL;DR: In this article , a review of the potential of different natural (polysaccharides, lipids, and proteins) and synthetic polymers for their film and coating-forming abilities considering their abundance, biological properties, and morphological and physiological features.

TL;DR: Zhang et al. as discussed by the authors proposed an automatic brain tumor MRI data segmentation framework which is called AGSE-VNet, where the Squeeze and Excite (SE) module is added to each encoder, the Attention Guide Filter (AG) module was added to every decoder, using the channel relationship to automatically enhance the useful information in the channel to suppress the useless information, and use the attention mechanism to guide the edge information and remove the influence of irrelevant information such as noise.

TL;DR: Zhang et al. as mentioned in this paper proposed an automatic brain tumor MRI data segmentation framework which is called AGSE-VNet, which uses the channel relationship to automatically enhance the useful information in the channel to suppress the useless information, and use the attention mechanism to guide the edge information and remove the influence of irrelevant information such as noise.