Summary of Efcm: Efficient Fine-tuning on Compressed Models For Deployment Of Large Models in Medical Image Analysis, by Shaojie Li and Zhaoshuo Diao
EFCM: Efficient Fine-tuning on Compressed Models for deployment of large models in medical image analysis
by Shaojie Li, Zhaoshuo Diao
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent development of deep learning models for medical image analysis and diagnosis has shown remarkable performance, but their large number of parameters causes memory and inference latency challenges. Knowledge distillation offers a solution to this issue. The proposed Efficient Fine-tuning on Compressed Models (EFCM) framework consists of two stages: unsupervised feature distillation and fine-tuning. In the distillation stage, Feature Projection Distillation (FPD) is used with a TransScan module for adaptive receptive field adjustment to enhance knowledge absorption capability of the student model. The study also compares three strategies (Reuse CLAM, Retrain CLAM, and End2end Train CLAM (ETC)) in the fine-tuning stage. Experimental results demonstrate that EFCM framework improves accuracy and efficiency in handling slide-level pathological image problems, effectively addressing challenges of deploying large medical models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study proposes a new way to use deep learning models for medical diagnosis. These models are very good at recognizing patterns in images, but they require a lot of computer memory and time to process. The researchers developed a new method that helps these models work better with less memory and time. They tested this method on different types of medical images and found it improved the accuracy and speed of diagnoses. |
Keywords
» Artificial intelligence » Deep learning » Distillation » Fine tuning » Inference » Knowledge distillation » Student model » Unsupervised
