Search Results for author:
Found 5 papers, 2 papers with code
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC).
Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations
Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instances that are not labeled as positive are considered negative samples.
CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation
Data augmentation is a widely used strategy that improves the training of CNNs, and the design of the augmentation method for brain lesion segmentation is still an open problem.
Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations
Usually, incomplete annotations can be achieved, where positive labeling results are carefully examined to ensure their reliability but there can be other positive instances, i. e., cells of interest, that are not included in the annotations.
Knowledge Transfer between Datasets for Learning-based Tissue Microstructure Estimation
Then, the interpolated signals are used together with the high-quality tissue microstructure computed from the source dataset to train deep networks that perform tissue microstructure estimation for the target dataset.
