Generative adversarial networks and adversarial methods in biomedical image analysis
Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game. Adversarial techniques have been extensively used to synthesize and analyze biomedical images. We provide an introduction to GANs and adversarial methods, with an overview of biomedical image analysis tasks that have benefited from such methods. We conclude with a discussion of strengths and limitations of adversarial methods in biomedical image analysis, and propose potential future research directions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks
Recent progress in biomedical image segmentation based on deep convolutional neural networks (CNNs) has drawn much attention. However, its vulnerability towards adversarial samples cannot be overlooked. This paper is the…
Image SegmentationLesion SegmentationSegmentationSemantic Segmentation+1A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis
In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring large image datasets to provide high-le…
Unsupervised learning for concept detection in medical images: a comparative analysis
As digital medical imaging becomes more prevalent and archives increase in size, representation learning exposes an interesting opportunity for enhanced medical decision support systems. On the other hand, medical imagin…
Information RetrievalRepresentation LearningRetrievalGenerative Adversarial Registration for Improved Conditional Deformable Templates
Deformable templates are essential to large-scale medical image registration, segmentation, and population analysis. Current conventional and deep network-based methods for template construction use only regularized regi…
Image RegistrationMedical Image RegistrationSpecificityHierarchical Self-Supervised Adversarial Training for Robust Vision Models in Histopathology
Adversarial attacks pose significant challenges for vision models in critical fields like healthcare, where reliability is essential. Although adversarial training has been well studied in natural images, its application…
Contrastive Learning