Alapfogalmak
Tokensome introduces a vision-language model for explainable and cognitive karyotyping, revolutionizing biomedical image analysis.
Kivonat
1. Introduction
AI advancements in medical diagnostics through deep learning techniques.
Challenges in integrating AI into clinical settings due to lack of model explainability.
Importance of karyotyping in detecting genetic abnormalities.
2. Method
Chromosome tokenization at the sub-chromosome level.
Sub-chromosome representation learning, positional encoding, and vision-language modeling.
3. Experiment
3.1 Classification
Evaluation using Dataset A and Dataset B.
Comparison with baseline methods and state-of-the-art models.
3.2 Abnormality Detection
Numerical vs structural anomalies detection.
Utilization of sub-chromosome representation for abnormality detection tasks.
3.3 Explainability
Enhancing explainability in segmentation, classification, and abnormality detection tasks.
4. Conclusion and Future Work
Tokensome's contribution to biomedical image analysis.
Integration plans for knowledge graphs, LLMs, and agent-based techniques.
Statisztikák
Tokensome elevates karyotyping method from visual perception to cognitive decision-making layer.
Our proposed approach achieves a classification accuracy of 98.96% on Dataset A.