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Found 15 papers, 6 papers with code
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
The FeTA Challenge 2024 advanced automated fetal brain MRI analysis by introducing biometry prediction as a new task alongside tissue segmentation.
In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning
Furthermore, this approach reduced memory consumption by up to 60% while delivering a 20% improvement in out-of-domain accuracy compared to conventional pattern-based fine-tuning.
Metasurface-generated large and arbitrary analog convolution kernels for accelerated machine vision
Recently, to address the challenges in processing speed and power consumption of conventional digital convolution operations, many optical components have been suggested to replace the digital convolution layer in the neural network, accelerating various machine vision tasks.
Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis
Directly training a post-processing model to mitigate topological errors often fails as such models tend to be biased towards the topological errors of a target segmentation network.
Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics
We uncover several strategies that inherently enhance the stability and generalizability of diffusion models for inverse problems and introduce a novel score-based diffusion framework, the \textbf{D}ynamics-aware S\textbf{D}E \textbf{D}iffusion \textbf{G}enerative \textbf{M}odel (D$^3$GM).
Weakly Supervised Learning of Cortical Surface Reconstruction from Segmentations
We also verify that CoSeg can extract high-quality cortical surfaces from fetal brain MRI on which traditional pipelines fail to produce acceptable results.
The Developing Human Connectome Project: A Fast Deep Learning-based Pipeline for Neonatal Cortical Surface Reconstruction
In this paper, we propose a fast deep learning (DL) based pipeline for dHCP neonatal cortical surface reconstruction, incorporating DL-based brain extraction, cortical surface reconstruction and spherical projection, as well as GPU-accelerated cortical surface inflation and cortical feature estimation.
DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images
Aircraft target detection in SAR images is a challenging task due to the discrete scattering points and severe background clutter interference.
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
Conditional Temporal Attention Networks for Neonatal Cortical Surface Reconstruction
The importance of each SVF, which is estimated by learned attention maps, is conditioned on the age of the neonates and varies with the time step of integration.
CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development.
Recovering medical images from CT film photos
While medical images such as computed tomography (CT) are stored in DICOM format in hospital PACS, it is still quite routine in many countries to print a film as a transferable medium for the purposes of self-storage and secondary consultation.
CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs
Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively.
CT Film Recovery via Disentangling Geometric Deformation and Illumination Variation: Simulated Datasets and Deep Models
We also record all accompanying information related to the geometric deformation (such as 3D coordinate, depth, normal, and UV maps) and illumination variation (such as albedo map).
Attention Based Glaucoma Detection: A Large-scale Database and CNN Model
The attention maps of the ophthalmologists are also collected in LAG database through a simulated eye-tracking experiment.
