A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging DOI Open Access
Deepshikha Bhati, Fnu Neha, Md Amiruzzaman

et al.

Published: Aug. 12, 2024

The combination of medical imaging and deep learning has significantly improved diagnostic prognostic capabilities in the healthcare domain. Nevertheless, inherent complexity models poses challenges understanding their decision-making processes. Interpretability visualization techniques have emerged as crucial tools to unravel black-box nature these models, providing insights into inner workings enhancing trust predictions. This survey paper comprehensively examines various interpretation applied imaging. reviews methodologies, discusses applications, evaluates effectiveness interpretability, reliability, clinical relevance image analysis.

Language: Английский

A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging DOI Creative Commons
Deepshikha Bhati, Fnu Neha, Md Amiruzzaman

et al.

Journal of Imaging, Journal Year: 2024, Volume and Issue: 10(10), P. 239 - 239

Published: Sept. 25, 2024

The combination of medical imaging and deep learning has significantly improved diagnostic prognostic capabilities in the healthcare domain. Nevertheless, inherent complexity models poses challenges understanding their decision-making processes. Interpretability visualization techniques have emerged as crucial tools to unravel black-box nature these models, providing insights into inner workings enhancing trust predictions. This survey paper comprehensively examines various interpretation applied imaging. reviews methodologies, discusses applications, evaluates effectiveness interpretability, reliability, clinical relevance image analysis.

Language: Английский

Citations

4
A Critical Review on Segmentation of Glioma Brain Tumor and Prediction of Overall Survival DOI
Novsheena Rasool, Javaid Iqbal Bhat

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 17, 2024

Language: Английский

Citations

4
FGA-Net: Feature-Gated Attention for Glioma Brain Tumor Segmentation in Volumetric MRI Images DOI
Novsheena Rasool, Javaid Iqbal Bhat, Niyaz Ahmad Wani

et al.

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 66 - 87

Published: Dec. 26, 2024

Language: Английский

Citations

3
Feasibility Study of Detecting and Segmenting Small Brain Tumors in a Small MRI Dataset with Self-Supervised Learning DOI Creative Commons
Weijun Zhang,

Wei-Teing Chen,

Chien‐Hung Liu

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(3), P. 249 - 249

Published: Jan. 22, 2025

Objectives: This paper studies the segmentation and detection of small metastatic brain tumors. study aims to evaluate feasibility training a deep neural network for tumors in MRI using very dataset 33 cases, by leveraging large public datasets primary tumors; Methods: explores various methods, including supervised learning, two transfer learning approaches, self-supervised utilizing U-net Swin UNETR models; Results: The approach model yielded best performance. Dice score was approximately 0.19. Sensitivity reached 100%, while specificity 54.5%. When excluding subjects with hyperintensities, improved 80.0%; Conclusions: It is feasible train

Language: Английский

Citations

0
SegSurvNet: SE-U-net-based glioma segmentation and overall survival prediction via MHA-NN and stacking regressor DOI
Novsheena Rasool, Javaid Iqbal Bhat

International Journal of Systems Assurance Engineering and Management, Journal Year: 2025, Volume and Issue: unknown

Published: June 5, 2025

Language: Английский

Citations

0
The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection DOI Creative Commons
Tarek Berghout

Journal of Imaging, Journal Year: 2024, Volume and Issue: 11(1), P. 2 - 2

Published: Dec. 24, 2024

Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early accurate diagnosis vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming error-prone. The rise of deep learning has led advanced models automated brain feature extraction, segmentation, classification. Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 papers past half-decade (2019-2024), this review fills that gap, exploring the latest methods paradigms, summarizing key concepts, challenges, datasets, offering insights into future directions using learning. This also incorporates an analysis previous targets three main aspects: results revealed primarily focuses on Convolutional Neural Networks (CNNs) their variants, with a strong emphasis transfer pre-trained models. Other Generative Adversarial (GANs) Autoencoders, used while Recurrent (RNNs) employed time-sequence modeling. Some integrate Internet Things (IoT) frameworks or federated real-time diagnostics privacy, paired optimization algorithms. However, adoption eXplainable AI (XAI) remains limited, despite its importance building trust diagnostics. Finally, outlines opportunities, focusing image quality, underexplored techniques, expanding deeper representations model behavior recurrent expansion advance imaging

Language: Английский

Citations

2
A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging DOI Open Access
Deepshikha Bhati, Fnu Neha, Md Amiruzzaman

et al.

Published: Aug. 12, 2024

The combination of medical imaging and deep learning has significantly improved diagnostic prognostic capabilities in the healthcare domain. Nevertheless, inherent complexity models poses challenges understanding their decision-making processes. Interpretability visualization techniques have emerged as crucial tools to unravel black-box nature these models, providing insights into inner workings enhancing trust predictions. This survey paper comprehensively examines various interpretation applied imaging. reviews methodologies, discusses applications, evaluates effectiveness interpretability, reliability, clinical relevance image analysis.

Language: Английский

Citations

1