Summary of Movl:exploring Fusion Strategies For the Domain-adaptive Application Of Pretrained Models in Medical Imaging Tasks, by Haijiang Tian et al.
MoVL:Exploring Fusion Strategies for the Domain-Adaptive Application of Pretrained Models in Medical Imaging Tasks
by Haijiang Tian, Jingkun Yue, Xiaohong Liu, Guoxing Yang, Zeyu Jiang, Guangyu Wang
First submitted to arxiv on: 13 May 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 This paper addresses the challenge of adapting natural language vision models to the medical domain. Medical images are more difficult to obtain than natural images, leading to limited datasets and hindering the training of strong pre-trained medical vision models. The authors propose a novel method called MoVL (Mixture of Visual Prompting and Linear Probe) that combines linear probing with visual prompting to bridge the gap between input medical images and natural pre-trained vision models. MoVL is designed as a joint learning loss function containing categorization loss and discrepancy loss, which measures the variance of prompted and plain images. The authors experiment on four medical image classification datasets using two mainstream architectures, ResNet and CLIP. Results show that MoVL can achieve full finetune accuracy (average 90.91%) without modifying the backbone model’s parameters or architecture. In fact, MoVL outperforms full fine-tuning (FF) on an out-of-distribution medical dataset by 5.18%. This paper contributes to the development of robust and efficient pre-trained medical vision models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve how computers understand medical images. Medical image datasets are hard to get because the equipment is specialized, so it’s challenging to train strong computer vision models for these images. The authors propose a new way called MoVL (Mixture of Visual Prompting and Linear Probe) that helps computers understand medical images better. They experiment with different methods on four medical image classification tasks using well-known computer architectures. The results show that MoVL can achieve great accuracy without changing the underlying model, which is important because it makes the process more efficient. |
Keywords
» Artificial intelligence » Fine tuning » Image classification » Loss function » Prompting » Resnet
