258 papers with code • 1 benchmarks • 1 datasets
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem
We compared four generic deep learning approaches trained on various datasets and two readily available lung segmentation algorithms.
We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics.
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning
This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis.
Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed treatment decisions.
The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing).
Due to its excellent performance, U-Net is the most widely used backbone architecture for biomedical image segmentation in the recent years.
Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction
Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumours.
We validate the generalizability of the proposed domain-independent segmentation approach on several datasets with varying parameters and machines.