DeepAI AI Chat
Log In Sign Up

Explainable Semantic Medical Image Segmentation with Style

by   Wei Dai, et al.

Semantic medical image segmentation using deep learning has recently achieved high accuracy, making it appealing to clinical problems such as radiation therapy. However, the lack of high-quality semantically labelled data remains a challenge leading to model brittleness to small shifts to input data. Most works require extra data for semi-supervised learning and lack the interpretability of the boundaries of the training data distribution during training, which is essential for model deployment in clinical practice. We propose a fully supervised generative framework that can achieve generalisable segmentation with only limited labelled data by simultaneously constructing an explorable manifold during training. The proposed approach creates medical image style paired with a segmentation task driven discriminator incorporating end-to-end adversarial training. The discriminator is generalised to small domain shifts as much as permissible by the training data, and the generator automatically diversifies the training samples using a manifold of input features learnt during segmentation. All the while, the discriminator guides the manifold learning by supervising the semantic content and fine-grained features separately during the image diversification. After training, visualisation of the learnt manifold from the generator is available to interpret the model limits. Experiments on a fully semantic, publicly available pelvis dataset demonstrated that our method is more generalisable to shifts than other state-of-the-art methods while being more explainable using an explorable manifold.


page 2

page 3

page 5

page 6

page 9

page 10


PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training

Deep learning based semi-supervised learning (SSL) methods have achieved...

Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation

Supervised learning-based segmentation methods typically require a large...

Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation

Generalising deep models to new data from new centres (termed here domai...

Quality-aware semi-supervised learning for CMR segmentation

One of the challenges in developing deep learning algorithms for medical...

Triple-View Feature Learning for Medical Image Segmentation

Deep learning models, e.g. supervised Encoder-Decoder style networks, ex...