Outputs (2)

LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint (2022)
Journal Article
Jafari, M., Francis, S., Garibaldi, J. M., & Chen, X. (2022). LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint. Medical Image Analysis, 81, Article 102536. https://doi.org/10.1016/j.media.2022.102536

In medical image segmentation, supervised machine learning models trained using one image modality (e.g. computed tomography (CT)) are often prone to failure when applied to another image modality (e.g. magnetic resonance imaging (MRI)) even for the... Read More about LMISA: A Lightweight Multi-modality Image Segmentation Network via Domain Adaptation using Gradient Magnitude and Shape Constraint.

FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net (2019)
Presentation / Conference Contribution
Jafari, M., Li, R., Xing, Y., Auer, D., Francis, S., Garibaldi, J., & Chen, X. (2019, August). FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net. Presented at 10th International Conference, ICIG 2019, Beijing, China

In this paper, we present a generic deep convolutional neural network (DCNN) for multi-class image segmentation. It is based on a well-established supervised end-to-end DCNN model, known as U-net. U-net is firstly modified by adding widely used batch... Read More about FU-Net: Multi-class Image Segmentation Using Feedback Weighted U-Net.