Data augmentation is commonly used to increase the size and diversity of the datasets in machine learning. It is of particular importance to evaluate the robustness of the existing machine learning methods. With progress in geometrical and 3D machine learning, many methods exist to augment a 3D object, from the generation of random orientations to exploring different perspectives of an object. In high-precision applications, the machine learning model must be robust with respect to the small perturbations of the input object.
The dataset used in the paper "A Deep Learning Approach for Segmentation, Classification and Visualization of 3D High Frequency Ultrasound Images of Mouse Embryos" is provided here. It contains both the segmentation and classification images with manual labels.
The dataset contains the ultrasound mouse embyro images with manual labels. For more detail, please look into each subfolder and the paper "A Deep Learning Approach for Segmentation, Classification and Visualization of 3D High Frequency Ultrasound Images of Mouse Embryos". Or you can contact the author by firstname.lastname@example.org if you have any question about the dataset and the paper. Thanks!
The Contest: Goals and Organisation
The 2019 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), the Johns Hopkins University (JHU), and the Intelligence Advanced Research Projects Activity (IARPA), aimed to promote research in semantic 3D reconstruction and stereo using machine intelligence and deep learning applied to satellite images.
- Participants to the benchmark are intended to submit:
- 2D semantic maps and nDSM/disparity/DSM maps in raster format (similar to the tif file of the training set) for Tracks 1, 2, and 3
- 3D semantic predictions in ASCII text files (similar to the text file of the training set) for Track 4
These results will be submitted to the Codalab competition websites for evaluation:
- Ranking among the participants will be based on:
- mIoU-3 for Tracks 1, 2, and 3
- mIoU for Track 4