Semi-supervised mp-MRI Data Synthesis with StitchLayer and Auxiliary Distance Maximization
In this paper, we address the problem of synthesizing multi-parameter magnetic resonance imaging (mp-MRI) data, i.e. Apparent Diffusion Coefficients (ADC) and T2-weighted (T2w), containing clinically significant (CS) prostate cancer (PCa) via semi-supervised adversarial learning. Specifically, our synthesizer generates mp-MRI data in a sequential manner: first generating ADC maps from 128-d latent vectors, followed by translating them to the T2w images. The synthesizer is trained in a semisupervised manner. In the supervised training process, a limited amount of paired ADC-T2w images and the corresponding ADC encodings are provided and the synthesizer learns the paired relationship by explicitly minimizing the reconstruction losses between synthetic and real images. To avoid overfitting limited ADC encodings, an unlimited amount of random latent vectors and unpaired ADC-T2w Images are utilized in the unsupervised training process for learning the marginal image distributions of real images. To improve the robustness of synthesizing, we decompose the difficult task of generating full-size images into several simpler tasks which generate sub-images only. A StitchLayer is then employed to fuse sub-images together in an interlaced manner into a full-size image. To enforce the synthetic images to indeed contain distinguishable CS PCa lesions, we propose to also maximize an auxiliary distance of Jensen-Shannon divergence (JSD) between CS and nonCS images. Experimental results show that our method can effectively synthesize a large variety of mpMRI images which contain meaningful CS PCa lesions, display a good visual quality and have the correct paired relationship. Compared to the state-of-the-art synthesis methods, our method achieves a significant improvement in terms of both visual and quantitative evaluation metrics.
Code (0)
등록된 구현이 없습니다.
Tasks
Synthesizing Multi-Parameter Magnetic Resonance Imaging (Mp-Mri) DataMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Auxiliary Deep Generative Models
Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which im…
Semi-supervised Semantic Segmentation using Auxiliary Network
Recently, the convolutional neural networks (CNNs) have shown great success on semantic segmentation task. However, for practical applications such as autonomous driving, the popular supervised learning method faces two …
Autonomous DrivingSegmentationSemantic SegmentationSemi-Supervised Semantic SegmentationComplementary consistency semi-supervised learning for 3D left atrial image segmentation
A network based on complementary consistency training, called CC-Net, has been proposed for semi-supervised left atrium image segmentation. CC-Net efficiently utilizes unlabeled data from the perspective of complementary…
Image SegmentationSegmentationSemantic SegmentationAutomatic diagnosis of cardiac magnetic resonance images based on semi-supervised learning
Cardiac magnetic resonance imaging (MRI) is a pivotal tool for assessing cardiac function. Precise segmentation of cardiac structures is imperative for accurate cardiac functional evaluation. This paper introduces a semi…
DiagnosticPredictionSegmentationSESQA: semi-supervised learning for speech quality assessment
Automatic speech quality assessment is an important, transversal task whose progress is hampered by the scarcity of human annotations, poor generalization to unseen recording conditions, and a lack of flexibility of exis…