[HTML][HTML] A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities
Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent
developments in deep neural networks have contributed to significant advances in medical …
developments in deep neural networks have contributed to significant advances in medical …
Segment anything in medical images
Medical image segmentation is a critical component in clinical practice, facilitating accurate
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …
Deep interactive segmentation of medical images: A systematic review and taxonomy
Interactive segmentation is a crucial research area in medical image analysis aiming to
boost the efficiency of costly annotations by incorporating human feedback. This feedback …
boost the efficiency of costly annotations by incorporating human feedback. This feedback …
Interactive medical image annotation using improved Attention U-net with compound geodesic distance
Y Zhang, J Chen, X Ma, G Wang, UA Bhatti… - Expert systems with …, 2024 - Elsevier
Accurate and massive medical image annotation data is crucial for diagnosis, surgical
planning, and deep learning in the development of medical images. However, creating large …
planning, and deep learning in the development of medical images. However, creating large …
Segment anything in medical images and videos: Benchmark and deployment
Recent advances in segmentation foundation models have enabled accurate and efficient
segmentation across a wide range of natural images and videos, but their utility to medical …
segmentation across a wide range of natural images and videos, but their utility to medical …
Scribbleprompt: fast and flexible interactive segmentation for any biomedical image
Biomedical image segmentation is a crucial part of both scientific research and clinical care.
With enough labelled data, deep learning models can be trained to accurately automate …
With enough labelled data, deep learning models can be trained to accurately automate …
Medical sam 2: Segment medical images as video via segment anything model 2
J Zhu, Y Qi, J Wu - arxiv preprint arxiv:2408.00874, 2024 - arxiv.org
Medical image segmentation plays a pivotal role in clinical diagnostics and treatment
planning, yet existing models often face challenges in generalization and in handling both …
planning, yet existing models often face challenges in generalization and in handling both …
Semantic hierarchy-aware segmentation
Humans are able to recognize structured relations in observation, allowing us to decompose
complex scenes into simpler parts and abstract the visual world at multiple levels. However …
complex scenes into simpler parts and abstract the visual world at multiple levels. However …
FFCA-YOLO for small object detection in remote sensing images
Y Zhang, M Ye, G Zhu, Y Liu, P Guo… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Issues, such as insufficient feature representation and background confusion, make
detection tasks for small object in remote sensing arduous. Particularly, when the algorithm …
detection tasks for small object in remote sensing arduous. Particularly, when the algorithm …
Clustering propagation for universal medical image segmentation
Prominent solutions for medical image segmentation are typically tailored for automatic or
interactive setups posing challenges in facilitating progress achieved in one task to another …
interactive setups posing challenges in facilitating progress achieved in one task to another …