Image Synthesis with a Convolutional Capsule Generative Adversarial Network- Prepared Data

A set of prepared datasets for running experiments to replicate paper (see below). List of data: Training capspix2pix: crops256.zip - folder containing 256x256 crops from the original dataset for training capspix2pix. Images are in the "train/original" folder, and labels are in the "train/mask" folder. syn256_x_data_val.npy + syn256_y_data_val.npy + syn256_y_points_data_val.npy (images + labels + centrelines) - validation synthetic dataset, used while training capspix2pix for plotting Training u-net: capspix2pix_AR_data_train.npy + capspix2pix_AR_mask_train.npy (images + labels) - data generated from a capspix2pix model from real labels capspix2pix_SSM_data_train.npy + capspix2pix_AR_mask_train.npy (images + labels) - data generated from a capspix2pix model from synthetic labels PBAM_SSM_data_train.npy + PBAM_SSM_mask_train.npy (images + labels) - data generated from PBAM (Physics-based model) for training u-net pix2pix_AR_data_train.npy + pix2pix_AR_mask_train.npy (images + labels) - data generated from a pix2pix model from real labels for training u-net pix2pix_SSM_data_train.npy + pix2pix_SSM_mask_train.npy (images + labels) - data generated from a pix2pix model from synthetic labels for training u-net real_data_data_train.npy + real_data_mask_train.npy (images + labels) - augmented real dataset for training u-net Testing u-net: org64_data_test.npy + org64_mask_test.npy (images + labels) - crops from original test dataset for testing u-net Interpolation: crops256_inter_data_train.npy + crops256_inter_mask_train.npy (images + labels) - example data for interpolation Please cite the following paper when using this dataset: Bass, C., Dai, T., Billot, B., Arulkumaran, K., Creswell, A., Clopath, C., De Paola, V., and Bharath, A. A., 2019. “Image synthesis with a convolutional capsule generative adversarial network,” Medial Imaging with Deep Learning. See Github page for further instructions: https://github.com/CherBass/CapsPix2Pix本数据集为复现下述论文的实验而准备的配套数据集。数据集明细如下： 1. 训练capspix2pix（CapsPix2Pix）专用数据集：crops256.zip——该压缩包包含源自原始数据集的256×256裁剪图像，用于训练capspix2pix模型。其中训练图像存放于`train/original`文件夹，标签（掩码）存放于`train/mask`文件夹。 2. 合成验证数据集：syn256_x_data_val.npy、syn256_y_data_val.npy与syn256_y_points_data_val.npy（分别对应图像、标签与中心线）——该数据集用于capspix2pix训练过程中的可视化绘图。 3. U-Net训练专用多组数据集： - capspix2pix_AR_data_train.npy + capspix2pix_AR_mask_train.npy（图像+标签）：由基于真实标签训练的capspix2pix模型生成的训练数据。 - capspix2pix_SSM_data_train.npy + capspix2pix_AR_mask_train.npy（图像+标签）：由基于合成标签训练的capspix2pix模型生成的训练数据。 - PBAM_SSM_data_train.npy + PBAM_SSM_mask_train.npy（图像+标签）：由PBAM（Physics-based model，基于物理的模型）生成的训练数据。 - pix2pix_AR_data_train.npy + pix2pix_AR_mask_train.npy（图像+标签）：由基于真实标签训练的pix2pix模型生成的训练数据。 - pix2pix_SSM_data_train.npy + pix2pix_SSM_mask_train.npy（图像+标签）：由基于合成标签训练的pix2pix模型生成的训练数据。 - real_data_data_train.npy + real_data_mask_train.npy（图像+标签）：经过数据增强的真实数据集。 4. U-Net测试专用数据集：org64_data_test.npy + org64_mask_test.npy（图像+标签）——源自原始测试数据集的裁剪图像，用于U-Net模型测试。 5. 插值任务示例数据集：crops256_inter_data_train.npy + crops256_inter_mask_train.npy（图像+标签）——用于插值任务的示例训练数据。 使用本数据集时，请引用下述论文：Bass, C., Dai, T., Billot, B., Arulkumaran, K., Creswell, A., Clopath, C., De Paola, V., and Bharath, A. A., 2019. "基于卷积胶囊生成对抗网络的图像合成（Image synthesis with a convolutional capsule generative adversarial network）", 《医学影像与深度学习（Medical Imaging with Deep Learning）》。更多操作说明请参见GitHub页面：https://github.com/CherBass/CapsPix2Pix