The Jamieson Lab specializes in designing machine learning algorithms and software tools for the following purposes:
- Representing complex spatiotemporal phenomena
- Spatial Biology (2D/3D, multimodal, & highly multiplexed tissue images)
- Advanced Image Analysis
Here is the video abstract for our study Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.
We used a generative deep network to encode latent representation of live-imaged melanoma cells. Supervised machine-learning algorithms classified metastatic efficiency using latent cell representations. We validated classifier prediction on melanoma cell lines in mouse xenografts and interpreted metastasis-driving features in amplified generative cell image models.
Our study also made the cover of the issue.
Additionally, we work closely with the Simulation Center at UT Southwestern for the following projects:
- Video-based machine learning (e.g., intraoperative robotic surgery analysis)
- Building AI/ML tools to enhance Medical Education/Training using multi-modal data streams (audio/speech, NLP, video)
We design machine learning algorithms that annotate and encode clinician-patient interactions so that accurate simulations of various scenarios can be produced for medical student education, practice, and testing.
The video here shows an example of our software annotating an interaction.
This video demonstrates our software annotating a doctor examining a patient via a mesh.