Minimally supervised medical semantic segmentation via unshared reconstructions and shared feature vectors

The research frontier in semi-supervised semantic image segmentation has recently shifted from the moderate supervision regimes towards minimal supervision, involving 1% to 5% labeled data. This is particularly relevant to medicine, where data annotation requires scarce expert clinical time. However, the extreme low-data setting introduces a unique set of mathematical and structural challenges that do not linearly scale down from their more moderate counterparts. In this work, we introduce a novel minimally supervised ($\sim$1% labeled samples) framework for semantic the segmentation of medical images. Our model learns meaningful representations through image reconstruction in an encoder–decoder network architecture setting. However, unlike conventional methods that generate a unique latent embedding per image, our architecture utilizes a novel sub-module, named the Vectorizer, and outputs a small set of scalar coefficients. These coefficients serve the purpose of generating a linear combination of trainable feature vectors shared across the dataset. This allows the proposed model to rely on prior shape knowledge accumulated throughout training in order to reconstruct each class. We tested our framework on challenging and diverse medical image datasets, surpassing or being comparable with the minimally supervised state of the art.