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Published on: October 28, 2018
Pyramid-based self-supervised learning for histopathological image classification.
Junjie Wang1, Hao Quan2, Chengguang Wang3
1Ningbo Artificial Intelligence Institute of Shanghai Jiao Tong University, Zhejiang 315000, PR China; Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China.
This study introduces the Pyramid-based Local Wavelet Transformer (PLWT), a self-supervised learning method for medical imaging. PLWT effectively extracts features from histopathology images, outperforming traditional methods in transferability and competitive performance.
Area of Science:
- Medical Imaging
- Computer Vision
- Machine Learning
Background:
- Supervised learning in medical imaging requires large labeled datasets, which are difficult to obtain for histopathology.
- Self-supervised learning (SSL) offers a solution by pre-training models on unlabeled data.
Purpose of the Study:
- To propose a novel self-supervised Pyramid-based Local Wavelet Transformer (PLWT) model for enhanced feature extraction in histopathology.
- To evaluate the effectiveness of PLWT in pre-training models for downstream tasks using unlabeled histopathological images.
Main Methods:
- Developed the PLWT model incorporating wavelet transforms to reduce information loss during feature extraction.
- Integrated a Local Squeeze-and-Excitation (Local SE) module with an inverse residual in the feedforward network to capture local image information.
- Pre-trained the model on a large dataset of unlabeled histopathology images using a self-supervised approach.
Main Results:
- PLWT demonstrated competitive performance compared to other SSL methods on histopathological image analysis.
- The transferability of visual representations learned by PLWT on histopathology images surpassed that of a supervised model trained on ImageNet.
- Wavelet-based downsampling significantly reduced information loss in feature transmission.
Conclusions:
- The proposed PLWT model effectively extracts rich local and global features from histopathology images using self-supervised learning.
- PLWT shows strong potential for improving medical image analysis by leveraging unlabeled data.
- Self-supervised pre-training with PLWT enhances the transferability of learned representations for histopathology tasks.
