Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends
Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …
CoTCoNet: An optimized coupled transformer-convolutional network with an adaptive graph reconstruction for leukemia detection
Background: Swift and accurate blood smear analyses are crucial for diagnosing leukemia
and other hematological malignancies. However, manual leukocyte count and …
and other hematological malignancies. However, manual leukocyte count and …
Enhanced pre-trained xception model transfer learned for breast cancer detection
Early detection and timely breast cancer treatment improve survival rates and patients'
quality of life. Hence, many computer-assisted techniques based on artificial intelligence are …
quality of life. Hence, many computer-assisted techniques based on artificial intelligence are …
Cross-level collaborative context-aware framework for medical image segmentation
Efficient and accurate medical image segmentation is necessary for pathological evaluation
and disease diagnosis in clinical practice. In recent years, the U-shaped encoder–decoder …
and disease diagnosis in clinical practice. In recent years, the U-shaped encoder–decoder …
[HTML][HTML] AMSC-Net: Anatomy and multi-label semantic consistency network for semi-supervised fluid segmentation in retinal OCT
Automated segmentation of pathological fluid regions is crucial for digital diagnosis and
individualized therapy under optical coherence tomography (OCT) images. Nonetheless …
individualized therapy under optical coherence tomography (OCT) images. Nonetheless …
Embedded AMIS-deep learning with dialog-based object query system for multi-class tuberculosis drug response classification
A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond
to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult …
to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult …
A lightweight multi-scale multi-angle dynamic interactive transformer-CNN fusion model for 3D medical image segmentation
X Hua, Z Du, H Yu, J Ma, F Zheng, C Zhang, Q Lu… - Neurocomputing, 2024 - Elsevier
Abstract Combining Convolutional Neural Network (CNN) and Transformer has become one
of the mainstream methods for three-dimensional (3D) medical image segmentation …
of the mainstream methods for three-dimensional (3D) medical image segmentation …
[HTML][HTML] Dilated dendritic learning of global–local feature representation for medical image segmentation
Medical image segmentation serves as an important tool in the treatment of various medical
diseases. However, achieving precise and efficient segmentation remains challenging due …
diseases. However, achieving precise and efficient segmentation remains challenging due …
A terrain segmentation network for navigable areas with global strip reliability evaluation and dynamic fusion
Accurate segmentation of safe navigable areas is crucial for scene parsing in autonomous
driving systems. However, existing segmentation methods often fail to fully leverage the …
driving systems. However, existing segmentation methods often fail to fully leverage the …
A progressive segmentation network for navigable areas with semantic–spatial information flow
Segmentation of safe navigable areas is a crucial technology for scene parsing in autopilot
systems. However, existing segmentation methods often fail to adequately exploit the …
systems. However, existing segmentation methods often fail to adequately exploit the …