Accurate segmentation of deep brain structures is critical for preoperative planning in such neurosurgical procedures as Deep Brain Stimulation (DBS). Previous research has showcased successful pipelines for segmentation from T1-weighted (T1w) Magnetic Resonance Imaging (MRI) data. Nevertheless, the role of T2-weighted (T2w) MRI data has been underexploited so far. This study proposes and evaluates a fully automated deep learning pipeline based on nnU-Net for the segmentation of eight clinically relevant deep brain structures. A heterogeneous dataset has been prepared by gathering 325 paired T1w and T2w MRI scans from eight publicly available sources, which have been annotated by means of an atlas-based registration approach. Three 3D nnU-Net models—unimodal T1w, unimodal T2w, and multimodal (encompassing both T1w and T2w)—have been trained and compared by using 5-fold cross-validation and a separate test set. The outcomes prove that the multimodal model consistently outperforms the T2w unimodal model and achieves comparable performance with the T1w unimodal model. On our dataset, all proposed models significantly exceed the performance of the state-of-the-art DBSegment tool. These findings underscore the value of multimodal MRI in enhancing deep brain segmentation and offer a robust framework for accurate delineation of subcortical targets in both research and clinical settings.

Artificial intelligence is increasingly used for genomic surveillance of infectious diseases, but predictive models must be evaluated under realistic generalization settings and interpreted cautiously. We present XAI-DENFormer, an explainability-oriented framework for dengue virus (DENV) serotype classification from complete viral genomes. A curated dataset of 29,091 publicly available DENV genomes was encoded as nucleotide-level one-hot sequences and processed with a chunk-based Transformer encoder designed for memory-efficient whole-genome analysis. Generalization was assessed using three complementary validation protocols: CD-HIT cluster-aware, geographical, and temporal validation. DENFormer variants were benchmarked against Logistic Regression, a feed-forward neural network, Longformer, and Performer under identical conditions. The mean-pooling DENFormer configuration maintained high performance across all three validation protocols, with accuracies of 98.68–99.91% across CD-HIT folds, 97.18–100.00% across geographical holdouts, and 96.35–99.86% across temporal holdouts. To inspect model behavior, we combined attention maps, Gradient × Input attributions, embedding visualizations, and regional statistical analyses. The resulting profiles supported distributed serotype-associated signals across structural, non-structural, and untranslated regions rather than reliance on a single genomic locus. XAI-DENFormer provides a robust and interpretable framework for large-scale DENV genome classification and model-guided genomic inspection.