Explainable and Robust Deep Learning for Liver Segmentation Through U-Net Network
<b>Background/Objectives:</b> Clinical imaging techniques, such as magnetic resonance imaging and computed tomography, play a vital role in supporting clinicians by aiding disease diagnosis and facilitating the planning of appropriate interventions. This is particularly important in mali...
Saved in:
| Main Authors: | Maria Chiara Brunese, Aldo Rocca, Antonella Santone, Mario Cesarelli, Luca Brunese, Francesco Mercaldo |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/15/7/878 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Method for Polyp Segmentation Through U-Net Network
by: Antonella Santone, et al.
Published: (2025-02-01) -
Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study
by: Valeria Sorgente, et al.
Published: (2025-06-01) -
BCSnet: A U-Net-Based Model for Segmentation of Brain Cells in Trypan Blue Images
by: Aleksei A. Kudryavtsev, et al.
Published: (2024-01-01) -
Performance and Efficiency Comparison of U-Net and Ghost U-Net in Road Crack Segmentation with Floating Point and Quantization Optimization
by: Haidhi Angkawijana Tedja, et al.
Published: (2024-12-01) -
Improved U-Net for Precise Gauge Dial Segmentation in Substation Inspection Systems: A Study on Enhancing Accuracy and Robustness
by: Wan Zou, et al.
Published: (2025-05-01)