qysoraka/Knowledge-Shift_MedImg-Analysis_KnoBo

KnoBo, a versatile solution to domain shifts in Medical Image Analysis with Knowledge Priors. This newfound approach signifies the importance of textbooks education in AI algorithms, especially when dealing complex issues in Medical Image Analysis.

Python

0

280 commits

updated Jul 26, 2024

See the code

README

A Textbook Remedy for Domain Shifts
Knowledge Priors for Medical Image Analysis

Paper | Project Page

Table of Contents

  1. CLIP Models
  2. Installation
  3. Quick Start
  4. Directories
  5. Extract Features
  6. Generate Bottlenecks from Medical Documents
  7. Train Grounding Functions
  8. Baselines

(All the original content here)

Citation

Please cite our paper if you find our work useful!

@article{yang2024textbook,
      title={A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis}, 
      author={Yue Yang and Mona Gandhi and Yufei Wang and Yifan Wu and Michael S. Yao and Chris Callison-Burch and James C. Gee and Mark Yatskar},
      journal={arXiv preprint arXiv:2405.14839},
      year={2024}
}

qysoraka/Knowledge-Shift_MedImg-Analysis_KnoBo

KnoBo, a versatile solution to domain shifts in Medical Image Analysis with Knowledge Priors. This newfound approach signifies the importance of textbooks education in AI algorithms, especially when dealing complex issues in Medical Image Analysis.

Python

0

280 commits

updated Jul 26, 2024

See the code

README

A Textbook Remedy for Domain Shifts
Knowledge Priors for Medical Image Analysis

Paper | Project Page

Table of Contents

  1. CLIP Models
  2. Installation
  3. Quick Start
  4. Directories
  5. Extract Features
  6. Generate Bottlenecks from Medical Documents
  7. Train Grounding Functions
  8. Baselines

(All the original content here)

Citation

Please cite our paper if you find our work useful!

@article{yang2024textbook,
      title={A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis}, 
      author={Yue Yang and Mona Gandhi and Yufei Wang and Yifan Wu and Michael S. Yao and Chris Callison-Burch and James C. Gee and Mark Yatskar},
      journal={arXiv preprint arXiv:2405.14839},
      year={2024}
}