受生物启发的半监督分割法，用无监督学习发现医学图像特征
Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging
- 第一阶段用赫布学习规则无监督训练卷积层和反卷积层
- 在少量标注数据上微调后性能超越现有最优方法
- 适合标注数据稀缺的医学图像分割任务
我们提出一种新型生物启发的半监督学习方法，用于训练下采样-上采样结构的语义分割模型。第一阶段不使用反向传播，而是利用赫布原理“一起放电，一起连接”作为局部学习规则，更新卷积层和转置卷积层的权重，实现对数据特征的无监督发现。第二阶段在少量标注数据上使用标准反向传播进行微调。我们在多个常用生物医学图像数据集上进行了实验，验证该方法在数据稀缺场景下的有效性。结果表明，该方法在不同标签比例下均优于当前最优方法。此外，使用该无监督阶段初始化现有最优方法也能带来性能提升。实验代码可在 https://github.com/ciampluca/hebbian-bootstraping-semi-supervised-medical-imaging 获取。
原文摘要 · Abstract (English)
We propose a novel bio-inspired semi-supervised learning approach for training downsampling-upsampling semantic segmentation architectures. The first stage does not use backpropagation. Rather, it exploits the Hebbian principle ``fire together, wire together'' as a local learning rule for updating the weights of both convolutional and transpose-convolutional layers, allowing unsupervised discovery of data features. In the second stage, the model is fine-tuned with standard backpropagation on a small subset of labeled data. We evaluate our methodology through experiments conducted on several widely used biomedical datasets, deeming that this domain is paramount in computer vision and is notably impacted by data scarcity. Results show that our proposed method outperforms SOTA approaches across different levels of label availability. Furthermore, we show that using our unsupervised stage to initialize the SOTA approaches leads to performance improvements. The code to replicate our experiments can be found at https://github.com/ciampluca/hebbian-bootstraping-semi-supervised-medical-imaging
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