Biologically-inspired semi-supervised semantic segmentation for biomedical imaging

We propose a novel bio-inspired two-stage semi-supervised learning approach for training semantic segmentation models based on downsampling-upsampling 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://tinyurl.com/hebbian-semantic-segmentation.