Journal
2021 IEEE 18TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
Volume -, Issue -, Pages 640-644Publisher
IEEE
DOI: 10.1109/ISBI48211.2021.9433993
Keywords
Bioimage analysis; segmentation; machine learning; convolutional neural networks; phase-contrast microscopy
Funding
- EMBL-EBI French Embassy Internship programme
- EMBL
Ask authors/readers for more resources
AURA-net is a convolutional neural network designed for segmenting phase-contrast microscopy images, utilizing transfer learning and Attention mechanisms to enhance training efficiency, suitable for small datasets. Additionally, it employs a loss function inspired by active contours to further improve performance.
We present AURA-net, a convolutional neural network (CNN) for the segmentation of phase-contrast microscopy images. AURA-net uses transfer learning to accelerate training and Attention mechanisms to help the network focus on relevant image features. In this way, it can be trained efficiently with a very limited amount of annotations. Our network can thus be used to automate the segmentation of datasets that are generally considered too small for deep learning techniques. AURA-net also uses a loss inspired by active contours that is well-adapted to the specificity of phase-contrast images, further improving performance. We show that AURA-net outperforms state-of-the-art alternatives in several small (less than 100 images) datasets.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available