Discovering distinctive elements of biomedical datasets for high-performance exploration
<|reference_start|>Discovering distinctive elements of biomedical datasets for high-performance exploration: The human brain represents an object by small elements and distinguishes two objects based on the difference in elements. Discovering the distinctive elements of high-dimensional datasets is therefore critical in numerous perception-driven biomedical and clinical studies. However, currently there is no available method for reliable extraction of distinctive elements of high-dimensional biomedical and clinical datasets. Here we present an unsupervised deep learning technique namely distinctive element analysis (DEA), which extracts the distinctive data elements using high-dimensional correlative information of the datasets. DEA at first computes a large number of distinctive parts of the data, then filters and condenses the parts into DEA elements by employing a unique kernel-driven triple-optimization network. DEA has been found to improve the accuracy by up to 45% in comparison to the traditional techniques in applications such as disease detection from medical images, gene ranking and cell recognition from single cell RNA sequence (scRNA-seq) datasets. Moreover, DEA allows user-guided manipulation of the intermediate calculation process and thus offers intermediate results with better interpretability.<|reference_end|>
@article{islam2024discovering,
    title={Discovering distinctive elements of biomedical datasets for
  high-performance exploration},
    author={Md Tauhidul Islam and Lei Xing},
    journal={arXiv preprint arXiv:2410.05436},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05436},
    primaryClass={cs.CV}
}