PL-Net: Progressive Learning Network for Medical Image Segmentation

TL;DR

PL-Net is a novel 2D medical image segmentation framework that progressively fuses coarse and fine semantic information without adding extra parameters, improving segmentation performance across multiple datasets.

Contribution

It introduces Internal and External Progressive Learning components that enhance feature fusion and semantic understanding without increasing model complexity.

Findings

Achieves competitive segmentation results on five datasets.

Does not add extra learnable parameters compared to U-Net variants.

Effectively fuses multi-scale semantic information through progressive learning stages.

Abstract

In recent years, deep convolutional neural network-based segmentation methods have achieved state-of-the-art performance for many medical analysis tasks. However, most of these approaches rely on optimizing the U-Net structure or adding new functional modules, which overlooks the complementation and fusion of coarse-grained and fine-grained semantic information. To address these issues, we propose a 2D medical image segmentation framework called Progressive Learning Network (PL-Net), which comprises Internal Progressive Learning (IPL) and External Progressive Learning (EPL). PL-Net offers the following advantages: (1) IPL divides feature extraction into two steps, allowing for the mixing of different size receptive fields and capturing semantic information from coarse to fine granularity without introducing additional parameters; (2) EPL divides the training process into two stages to optimize parameters and facilitate the fusion of coarse-grained information in the first stage and fine-grained information in the second stage. We conducted comprehensive evaluations of our proposed method on five medical image segmentation datasets, and the experimental results demonstrate that PL-Net achieves competitive segmentation performance. It is worth noting that PL-Net does not introduce any additional learnable parameters compared to other U-Net variants.