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Fine-grained prompt tuning plus (FPT+) is a new parameter-efficient transfer learning method for medical image classification. FPT+ significantly reduces memory consumption and outperforms other methods while using minimal parameters.

Area of Science:

Artificial Intelligence
Computer Vision
Medical Imaging

Background:

Large-scale pretrained models are crucial for downstream tasks, but full fine-tuning is computationally expensive.
Parameter-efficient transfer learning (PETL) offers a cost-effective alternative, yet faces challenges with increasing model and input sizes, particularly in memory consumption.
High-resolution medical image classification demands efficient adaptation methods due to large data requirements.

Purpose of the Study:

Introduce fine-grained prompt tuning plus (FPT+), a novel PETL method.
Address the challenge of high memory consumption in PETL for high-resolution medical image classification.
Demonstrate FPT+'s effectiveness in reducing memory and parameter usage while maintaining performance.

Main Methods:

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Published on: November 30, 2022

FPT+ utilizes a lightweight side network and a frozen large pretrained model (LPM).
High-resolution images are processed by the LPM for feature extraction, while downsampled images train the side network to minimize memory.
Fine-grained prompts and fusion modules enable the side network to leverage LPM's intermediate activations.

Main Results:

FPT+ significantly reduces training memory consumption compared to existing PETL methods.
Achieved superior performance on eight diverse medical image datasets.
Required only 1.03% of learnable parameters and 3.18% of memory compared to full fine-tuning of a ViT-B model.

Conclusions:

FPT+ presents a highly efficient PETL approach for high-resolution medical image classification.
The method effectively balances performance, parameter efficiency, and memory reduction.
FPT+ offers a practical solution for adapting large models to medical imaging tasks with limited resources.