DeepAI AI Chat
Log In Sign Up

A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation

by   Ruizhe Li, et al.

Deep learning based image segmentation has achieved the state-of-the-art performance in many medical applications such as lesion quantification, organ detection, etc. However, most of the methods rely on supervised learning, which require a large set of high-quality labeled data. Data annotation is generally an extremely time-consuming process. To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN). An encoder-decoder based DCNN is initially trained using a few annotated training samples. This initially trained model is then copied into sub-models and improved iteratively using random subsets of unlabeled data with pseudo labels generated from models trained in the previous iteration. The number of sub-models is gradually decreased to one in the final iteration. We evaluate the proposed method on a public grand-challenge dataset for skin lesion segmentation. Our method is able to significantly improve beyond fully supervised model learning by incorporating unlabeled data.


Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation

Deep convolutional neural networks have achieved remarkable progress on ...

Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles

Auto-annotation by ensemble of models is an efficient method of learning...

Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach

Contemporary deep learning based medical image segmentation algorithms r...

Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model

Automatic skin lesion segmentation on dermoscopic images is an essential...

A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision

Standard segmentation of medical images based on full-supervised convolu...

Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers

Polyp segmentation is a crucial step towards computer-aided diagnosis of...

Code Repositories