Multi-task deep learning for medical image computing and analysis: A review
The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …
conventional deep learning models are constructed for a single specific task, multi-task deep …
Vision transformers for computational histopathology
Computational histopathology is focused on the automatic analysis of rich phenotypic
information contained in gigabyte whole slide images, aiming at providing cancer patients …
information contained in gigabyte whole slide images, aiming at providing cancer patients …
Interpretable laryngeal tumor grading of histopathological images via depth domain adaptive network with integration gradient CAM and priori experience-guided …
Tumor grading and interpretability of laryngeal cancer is a key yet challenging task in the
clinical diagnosis, mainly because of the commonly used low-magnification pathological …
clinical diagnosis, mainly because of the commonly used low-magnification pathological …
A survey of Transformer applications for histopathological image analysis: New developments and future directions
CC Atabansi, J Nie, H Liu, Q Song, L Yan… - BioMedical Engineering …, 2023 - Springer
Transformers have been widely used in many computer vision challenges and have shown
the capability of producing better results than convolutional neural networks (CNNs). Taking …
the capability of producing better results than convolutional neural networks (CNNs). Taking …
Skeletal fracture detection with deep learning: A comprehensive review
Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray
images. However, challenges remain that hinder progress in this field. Firstly, a lack of clear …
images. However, challenges remain that hinder progress in this field. Firstly, a lack of clear …
Continuous refinement-based digital pathology image assistance scheme in medical decision-making systems
J Wu, T Luo, J Zeng, F Gou - IEEE Journal of Biomedical and …, 2024 - ieeexplore.ieee.org
Digital pathology images' extensive cellular information provide a trustworthy foundation for
tumor diagnosis. With the aid of computer-aided diagnostics, pathologists can locate crucial …
tumor diagnosis. With the aid of computer-aided diagnostics, pathologists can locate crucial …
UniVisNet: A unified visualization and classification network for accurate grading of gliomas from MRI
Y Zheng, D Huang, X Hao, J Wei, H Lu, Y Liu - Computers in Biology and …, 2023 - Elsevier
Accurate grading of brain tumors plays a crucial role in the diagnosis and treatment of
glioma. While convolutional neural networks (CNNs) have shown promising performance in …
glioma. While convolutional neural networks (CNNs) have shown promising performance in …
Explainable Deep Learning Approach for Multi-Class Brain Magnetic Resonance Imaging Tumor Classification and Localization Using Gradient-Weighted Class …
Brain tumors (BT) present a considerable global health concern because of their high
mortality rates across diverse age groups. A delay in diagnosing BT can lead to death …
mortality rates across diverse age groups. A delay in diagnosing BT can lead to death …
DCA-DAFFNet: An End-to-end Network with Deformable Fusion Attention and Deep Adaptive Feature Fusion for Laryngeal Tumor Grading from Histopathology …
Laryngeal tumor grading is a challenging task for computer-aided clinical diagnosis (CACD),
mainly because the nuclei in histopathological images have large differences in shape and …
mainly because the nuclei in histopathological images have large differences in shape and …
A CAD system for automatic dysplasia grading on H&E cervical whole-slide images
Cervical cancer is the fourth most common female cancer worldwide and the fourth leading
cause of cancer-related death in women. Nonetheless, it is also among the most …
cause of cancer-related death in women. Nonetheless, it is also among the most …