DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images
Diabetic retinopathy (DR) is a complication of diabetes that severely affects eyes. It can be graded into five levels of severity according to international protocol. However, optimizing a grading model to have strong generalizability requires a large amount of balanced training data, which is difficult to collect particularly for the high severity levels. Typical data augmentation methods, including random flipping and rotation, cannot generate data with high diversity. In this paper, we propose a diabetic retinopathy generative adversarial network (DR-GAN) to synthesize high-resolution fundus images which can be manipulated with arbitrary grading and lesion information. Thus, large-scale generated data can be used for more meaningful augmentation to train a DR grading and lesion segmentation model. The proposed retina generator is conditioned on the structural and lesion masks, as well as adaptive grading vectors sampled from the latent grading space, which can be adopted to control the synthesized grading severity. Moreover, a multi-scale spatial and channel attention module is devised to improve the generation ability to synthesize details. Multi-scale discriminators are designed to operate from large to small receptive fields, and joint adversarial losses are adopted to optimize the whole network in an end-to-end manner. With extensive experiments evaluated on the EyePACS dataset connected to Kaggle, as well as the FGADR dataset, we validate the effectiveness of our method, which can both synthesize highly realistic (1280 x 1280) controllable fundus images and contribute to the DR grading task.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationGenerative Adversarial NetworkLesion SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images
One of the most challenges in medical imaging is the lack of data. It is proven that classical data augmentation methods are useful but still limited due to the huge variation in images. Using generative adversarial netw…
Data AugmentationGenerative Adversarial NetworkConditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation
We propose a new generative adversarial architecture to mitigate imbalance data problem in medical image semantic segmentation where the majority of pixels belongs to a healthy region and few belong to lesion or non-heal…
Cell SegmentationEnsemble LearningLesion SegmentationSegmentation+1Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network
Deep Neural Networks (DNNs) show a significant impact on medical imaging. One significant problem with adopting DNNs for skin cancer classification is that the class frequencies in the existing datasets are imbalanced. T…
Cancer ClassificationData AugmentationGeneral ClassificationGenerative Adversarial Network+1Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults. DR lesions can be challenging to identify in fundus images, and automatic DR detection systems can offer strong clinical value. Of the publ…
Generative Adversarial NetworkLesion SegmentationSegmentationSemantic SegmentationLesion Conditional Image Generation for Improved Segmentation of Intracranial Hemorrhage from CT Images
Data augmentation can effectively resolve a scarcity of images when training machine-learning algorithms. It can make them more robust to unseen images. We present a lesion conditional Generative Adversarial Network LcGA…
Computed Tomography (CT)Conditional Image GenerationData AugmentationGenerative Adversarial Network+3