Peering into the Heart: A Comprehensive Exploration of Semantic Segmentation and Explainable AI on the MnMs-2 Cardiac MRI Dataset
Accurate and interpretable segmentation of medical images is crucial for computer-aided diagnosis and image-guided interventions. This study explores the integration of semantic segmentation and explainable AI techniques on the MnMs-2 Cardiac MRI dataset. We propose a segmentation model that achieve...
Saved in:
Main Authors: | Ayoob Mohamed, Nettasinghe Oshan, Sylvester Vithushan, Bowala Helmini, Mohideen Hamdaan |
---|---|
Format: | Article |
Language: | English |
Published: |
Sciendo
2025-01-01
|
Series: | Applied Computer Systems |
Subjects: | |
Online Access: | https://doi.org/10.2478/acss-2025-0002 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Pre Hoc and Co Hoc Explainability: Frameworks for Integrating Interpretability into Machine Learning Training for Enhanced Transparency and Performance
by: Cagla Acun, et al.
Published: (2025-07-01) -
Explainable AI for Spectral Analysis of Electromagnetic Fields
by: Dimitris Kalatzis, et al.
Published: (2025-01-01) -
A Comprehensive Review of Explainable Artificial Intelligence (XAI) in Computer Vision
by: Zhihan Cheng, et al.
Published: (2025-07-01) -
A note on certain implications of clinical artificial intelligences for the field of medico-legal semiotics
by: CAROLE SENECHAL, et al.
Published: (2025-06-01) -
Explainable Artificial Intelligence in the Field of Drug Research
by: Ding Q, et al.
Published: (2025-05-01)