Transformers and other Attention-Based Algorithms for Biomedicine
[EN] This research explores the application of Transformer-based models and Hybrid Attention Mechanisms across three critical biomedical tasks: meningioma segmentation, epileptic seizure detection, and pathogenicity prediction of genomic variants. For meningioma segmentation, this research investiga...
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| Tipo de recurso: | tesis doctoral |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/167164 |
| Acceso en línea: | http://hdl.handle.net/10366/167164 |
| Access Level: | acceso abierto |
| Palabra clave: | Tesis y disertaciones académicas Universidad de Salamanca (España) Tesis Doctoral Academic dissertations Resonancia magnética Epilepsia Inteligencia artificial 1203.04 Inteligencia Artificial 3314 Tecnología Médica 1209.03 Análisis de Datos |
| Sumario: | [EN] This research explores the application of Transformer-based models and Hybrid Attention Mechanisms across three critical biomedical tasks: meningioma segmentation, epileptic seizure detection, and pathogenicity prediction of genomic variants. For meningioma segmentation, this research investigates refining skip connections in a U-Net architecture by incorporating Swin Transformers for Magnetic Resonance Imaging. The proposed SwinC U-Net model, evaluated on the Brain Tumor Segmentation Meningioma 2023 challenge dataset, achieved a Dice score of 0.8933±0.0016, precision of 0.9020±0.0041, and recall of 0.8939±0.0044, surpassing U-Net and Attention U-Net in precision and accuracy, while slightly underperforming in recall compared to U-Net. In epileptic seizure detection, this work addresses the challenge of real-time monitoring using microelectrode array data from rat brain slices with 4-aminopyridine-induced epileptic activity. Lightweight neural models were trained to detect seizures. The best performing model, GRU+Attention, achieved an event-level F1 score of 0.839, Jaccard index of 0.722, recall of 0.813, and precision of 0.867 on a held-out test set. The model was quantized to INT8, maintaining high event-level detection performance (F1 = 0.812, Jaccard = 0.709) and demonstrating real-time feasibility on embedded platforms like the Raspberry Pi 5 (latency: 2.61 ms) and Coral Dev Board (latency: 23.7 ms), with memory usage under 614 MB. For pathogenicity prediction, a Feature Tokenizer Transformer model was developed to classify genetic variants. The model used a set of inputs derived from next-generation sequencing data from the CLINVAR database, after being processed through the regular pipeline consisting of quality control, alignment, variant calling, filtering, indexing, and annotation. The semi-supervised approach allowed the model to extract insight from uncertain-labeled data. The model achieved a precision of 0.98, sensitivity of 0.92, specificity of 0.99, and F1 score of 0.95, outperforming ClinPred and REVEL in precision and specificity, but with slightly lower sensitivity than ClinPred. These studies demonstrate the versatility and efficacy of Attention-based models in handling diverse biomedical signal modalities. The results underscore the potential of Transformer architectures to advance biomedical data analysis, enhancing diagnostics and personalized medicine. |
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