Background: 3D medical image segmentation is a cornerstone for quantitative analysis and clinical decision-making in various modalities. However, acquiring high-quality voxel-level annotations is both ...
Abstract: Brain tumor segmentation plays a critical role in accurate diagnosis and treatment planning but remains challenging due to complex tumor boundaries and variations across MRI sequences.
Abstract: Convolutional neural networks (CNNs) have emerged as a preferred approach for medical image analysis. The dimensionality of images is a principal factor in CNN models, as they are designed ...
Background: Single-cell multi-omics technologies capture cellular heterogeneity at unprecedented resolution, yet dimensionality reduction methods face a fundamental local–global trade-off: approaches ...
The question of global regularity for the three-dimensional incompressible Navier-Stokes equations remains one of the deepest challenges in mathematical physics [1]. The central difficulty arises from ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results