Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance Learning
In the context of histological image classification, Multiple Instance Learning (MIL) methods only require labels at Whole Slide Image (WSI) level, effectively reducing the annotation bottleneck. However, for their deployment in real scenarios, they must be able to detect the presence of previously...
Saved in:
| Main Authors: | Francisco Javier Saez-Maldonado, Luz Garcia, Lee A. D. Cooper, Jeffery A. Goldstein, Rafael Molina, Aggelos K. Katsaggelos |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11098836/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Generative autoencoder to prevent overregularization of variational autoencoder
by: YoungMin Ko, et al.
Published: (2025-02-01) -
Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models
by: Renato Melo, et al.
Published: (2025-03-01) -
Unsupervised Anomaly Detection With Variational Autoencoders Applied to Full‐Disk Solar Images
by: Marius Giger, et al.
Published: (2024-02-01) -
Meta-learning approach for variational autoencoder hyperparameter tuning
by: Michele Berti, et al.
Published: (2025-06-01) -
Variational Autoencoder Transfer Functions for Onshore Tsunami Hazard Curves
by: Willington Renteria, et al.
Published: (2025-06-01)