XAI-Driven Deep Learning for Protein Sequence Functional Group Classification

A new framework called xAiTrees has been introduced to enhance the interpretability of Convolutional Neural Networks (CNNs) by utilizing hierarchical segmentation techniques. This method aims to provide faithful explanations of neural network reasoning, addressing challenges faced by existing explainable AI (xAI) methods like Integrated Gradients and LIME, which often produce noisy or misleading outputs.

A new study has introduced an explainable two-stage deep learning framework for assessing pericoronitis in panoramic radiographs, utilizing YOLOv8 for anatomical localization and a modified ResNet-50 for pathological classification. The system achieved high precision and alignment with radiologists' diagnostic impressions, enhancing interpretability through Grad-CAM visualizations.

A novel deep learning framework named TIMM-ProRS has been introduced to enhance the prognosis and risk stratification of diabetic retinopathy (DR), a condition that threatens the vision of millions worldwide. This framework integrates Vision Transformer, Convolutional Neural Network, and Graph Neural Network technologies, utilizing both retinal images and temporal biomarkers to achieve a high accuracy rate of 97.8% across multiple datasets.

A new study has developed a pre-trained foundation model framework aimed at improving the classification of extramural vascular invasion (EVI) and mesorectal fascia invasion (MFI) in rectal cancer using multiplanar MRI scans. This framework was evaluated using 331 pre-treatment T2-weighted MRI scans from three European hospitals, addressing the limitations of subjective visual assessments and inter-institutional variability in diagnostics.

The introduction of HiFi-Mamba, a dual-stream Mamba-based architecture, aims to enhance high-fidelity MRI reconstruction from undersampled k-space data by addressing key limitations of existing Mamba variants. The architecture features stacked W-Laplacian and HiFi-Mamba blocks, which separate low- and high-frequency streams to improve image fidelity and detail.