@inproceedings{yu2023diffusion, title={Diffusion-Based Data Augmentation for Nuclei Image Segmentation}, author={Yu, Xinyi and Li, Guanbin and Lou, Wei and Liu, Siqi and Wan, Xiang and Chen, Yan and Li, Haofeng}
Learning object placement by in- painting for compositional data augmentation. In European Conference on Computer Vision, pages 566–581. Springer, 2020. 2 [81] Lingzhi Zhang, Tarmily Wen, and Jianbo Shi. Deep image blending. In Proceedings of the IEEE/CVF Winter...
Self-supervised learning with data augmentations provably isolates content from style. Proc. NeurIPS 2021, 34, 16451–16467. [Google Scholar] Sturma, N.; Squires, C.; Drton, M.; Uhler, C. Unpaired multi-domain causal representation learning. Adv. Neural Inf. Process. Syst. 2024, 36. [...
Official repository for 3D multimodal MRI synthesis with conditional slice-based latent diffusion models for data augmentation in tumor segmentation - Arksyd96/multi-modal-mri-and-mask-synthesis-with-conditional-slice-based-ldm
Image normalization was applied to ensure uniformly distributed data. And no additional data augmentation methods are used. During training, an initial learning rate of 0.0001 was set, and the AdamW optimizer was chosen to decay weights, ensuring smooth loss reduction while accelerating convergence. ...
To address the first shortcoming, we suggest a diffusion-based data augmentation method that employs ChatGPT and AudioLDM. Also, to address the second concern, we put forward a two-stage self-supervised model. In the first stage, we introduce a novel approach that combines Contrastive learning ...
Artificial data augmentation does not necessarily mirror the biological heterogeneity of strokes, and imperfectly reflects the noise, low resolution, and technical variability of clinical data. Models developed in modest and homogeneous samples, or in artificial augmented data, might be less efficient in...
Reference image augmentation reason: x_r 几乎来自于x_s ,不符合测试场景的情况。 solusion: 对 x_r 采用里多种数据增强技术(filp,rotation,blur,elastic transform)A : data augmentation Mask shape augmentation reason:mask region m 不一定完整包含一个对象 solution: 基于bounding box 生成一个任意形状的掩...
Training, testing and validation processes were done patch-wise (patch size of 32 \(\times\) 32) after augmentation with random rotations. Each of the down and up streams paths in the network had 4 convolutional layers. A leaning rate of 0.001 was used. We applied the trained network on ...
We incorporate data augmentation including ran- dom rotation [−45◦, 45◦], random scale [0.65, 1.35], trun- cation (half body), and flipping during training. The time interval δ is set to 2. We define the total sampling steps T = 1000. We a...