Keywords
vision-language models
Abstract
Biomedical monitoring systems increasingly rely on continuous visual observation, multimodal interpretation, and efficient representation learning. This topic focuses on token-efficient biomedical monitoring by combining forensic vision-language compression, instance-aware super-resolution, hyperspectral-LiDAR fusion, melanoma detection, breast cancer imaging, and experimental animal monitoring. Token compression reduces computational burden in long-duration visual monitoring, while instance-level enhancement improves the visibility of subtle biomedical changes. Computer vision methods for vaccine side-effect monitoring illustrate the importance of automated observation in experimental settings. Medical image segmentation and melanoma detection provide related high-stakes applications where small visual details are clinically meaningful. The literature structure supports biomedical monitoring systems that balance visual fidelity, computational efficiency, and clinically interpretable evidence.
References
Dai, Y., Chen, Z., Pradeepkumar, J., Matsubara, Y., Sun, J., Sakurai, Y., & Dong, Y. (2026). EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild. arXiv preprint arXiv:2605.09505.
Yang, J. X., Wang, J., Li, Z., Sui, C., Long, Z., & Zhou, J. (2025). HSLiNets: Evaluating Band Ordering Strategies in Hyperspectral and LiDAR Fusion. IEEE Geoscience and Remote Sensing Letters, 22, 1–5, Article 5505605. https://doi.org/10.1109/LGRS.2025.3567626
Lai, Y., Yu, Z., Wang, J., Shen, L., Xu, Y., & Cao, X. (2026). ForensicZip: More Tokens are Better but Not Necessary in Forensic Vision-Language Models. arXiv preprint arXiv:2603.12208.
Guo, Z., Zhao, K., & Zhang, L. (2026). InstanceRSR: Real-World Super-Resolution via Instance-Aware Representation Alignment. ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain, 10577–10581. https://doi.org/10.1109/ICASSP55912.2026.11462690
Xie, S., Xu, L., Lei, C., Wang, J., Wang, J., Wang, Z., Sun, Y., Li, D., Li, F., Lin, R., et al. (2026). RST2G: Residual-Guided Spatiotemporal Transformer Graph Fusion Enhancement for Breast Cancer Segmentation in DCE-MRI. Cyborg and Bionic Systems, 7, 0502.
Liu, Y., Li, C., Li, F., Lin, R., Zhang, D., & Lian, Y. (2025). Advances in computer vision and deep learning-facilitated early detection of melanoma. Briefings in Functional Genomics, 24, elaf002.
Li, C., Shao, S., Mikason, W., Lin, R., & Liu, Y. (2024). Utilizing computer vision for continuous monitoring of vaccine side effects in experimental mice. arXiv preprint arXiv:2404.03121.
Woo, S., Park, J., Lee, J. Y., & Kweon, I. S. (2018). CBAM: Convolutional Block Attention Module. European Conference on Computer Vision, 3–19.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of CVPR 2016, 770–778.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. MICCAI 2015, 234–241.