Rethinking Transfer Learning for Medical Image Classification

Understand

Transfer learning (TL) from pretrained deep models is a standard practice in modern medical image classification (MIC).

• However, what levels of features to be reused are problem-dependent, and uniformly finetuning all layers of pretrained models may be suboptimal.
• This insight has partly motivated the recent differential TL strategies, such as TransFusion (TF) and layer-wise finetuning (LWFT), which treat the layers in the pretrained models differentially.
• In this paper, we add one more strategy into this family, called TruncatedTL, which reuses and finetunes appropriate bottom layers and directly discards the remaining layers.