Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases DOI
Sanad Aburass, Osama Dorgham, Jamil Al Shaqsi

и другие.

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Март 31, 2025

The rapid advancement of artificial intelligence techniques, particularly deep learning, has transformed medical imaging. This paper presents a comprehensive review recent research that leverage vision transformer (ViT) models for image classification across various disciplines. fields focus include breast cancer, skin lesions, magnetic resonance imaging brain tumors, lung diseases, retinal and eye analysis, COVID-19, heart colon disorders, diabetic retinopathy, kidney lymph node bone analysis. Each work is critically analyzed interpreted with respect to its performance, data preprocessing methodologies, model architecture, transfer learning interpretability, identified challenges. Our findings suggest ViT shows promising results in the domain, often outperforming traditional convolutional neural networks (CNN). A overview presented form figures tables summarizing key from each field. provides critical insights into current state using highlights potential future directions this rapidly evolving area.

Язык: Английский

Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases DOI
Sanad Aburass, Osama Dorgham, Jamil Al Shaqsi

и другие.

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Март 31, 2025

The rapid advancement of artificial intelligence techniques, particularly deep learning, has transformed medical imaging. This paper presents a comprehensive review recent research that leverage vision transformer (ViT) models for image classification across various disciplines. fields focus include breast cancer, skin lesions, magnetic resonance imaging brain tumors, lung diseases, retinal and eye analysis, COVID-19, heart colon disorders, diabetic retinopathy, kidney lymph node bone analysis. Each work is critically analyzed interpreted with respect to its performance, data preprocessing methodologies, model architecture, transfer learning interpretability, identified challenges. Our findings suggest ViT shows promising results in the domain, often outperforming traditional convolutional neural networks (CNN). A overview presented form figures tables summarizing key from each field. provides critical insights into current state using highlights potential future directions this rapidly evolving area.

Язык: Английский

Процитировано

0