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

и другие.

Опубликована: Авг. 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.

Язык: 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

и другие.

Journal of Imaging, Год журнала: 2024, Номер 10(10), С. 239 - 239

Опубликована: Сен. 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.

Язык: Английский

Процитировано

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, Год журнала: 2024, Номер unknown

Опубликована: Окт. 17, 2024

Язык: Английский

Процитировано

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

и другие.

Communications in computer and information science, Год журнала: 2024, Номер unknown, С. 66 - 87

Опубликована: Дек. 26, 2024

Язык: Английский

Процитировано

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

и другие.

Diagnostics, Год журнала: 2025, Номер 15(3), С. 249 - 249

Опубликована: Янв. 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

Язык: Английский

Процитировано

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, Год журнала: 2025, Номер unknown

Опубликована: Июнь 5, 2025

Язык: Английский

Процитировано

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, Год журнала: 2024, Номер 11(1), С. 2 - 2

Опубликована: Дек. 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

Язык: Английский

Процитировано

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

и другие.

Опубликована: Авг. 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.

Язык: Английский

Процитировано

1