Learning Domain-Invariant Representations of Histological Images
Histological images present high appearance variability due to inconsistent latent parameters related to the preparation and scanning procedure of histological slides, as well as the inherent biological variability of tissues. Machine-learning models are trained with images from a limited set of dom...
Main Authors: | Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Mitko Veta |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2019-07-01
|
Series: | Frontiers in Medicine |
Subjects: | |
Online Access: | https://www.frontiersin.org/article/10.3389/fmed.2019.00162/full |
Similar Items
-
NuSegDA: Domain adaptation for nuclei segmentation
by: Mohammad Minhazul Haq, et al.
Published: (2023-03-01) -
Learning Domain-Invariant Discriminative Features for Heterogeneous Face Recognition
by: Shanmin Yang, et al.
Published: (2020-01-01) -
Learning Deep Representations of Cardiac Structures for 4D Cine MRI Image Segmentation through Semi-Supervised Learning
by: S. M. Kamrul Hasan, et al.
Published: (2022-11-01) -
Automatic Seizure Classification Based on Domain-Invariant Deep Representation of EEG
by: Xincheng Cao, et al.
Published: (2021-10-01) -
Inter-Domain Invariant Cross-Domain Object Detection Using Style and Content Disentanglement for In-Vehicle Images
by: Zhipeng Jiang, et al.
Published: (2024-01-01)