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Found 15 papers, 10 papers with code
Extremely weakly-supervised blood vessel segmentation with physiologically based synthesis and domain adaptation
Accurate analysis and modeling of renal functions require a precise segmentation of the renal blood vessels.
Localise to segment: crop to improve organ at risk segmentation accuracy
Some deep learning methods for the segmentation of organs at risk use a two stage process where a localisation network first crops an image to the relevant region and then a locally specialised network segments the cropped organ of interest.
Deep Learning-Assisted Localisation of Nanoparticles in synthetically generated two-photon microscopy images
Low signal-to-noise ratios, movement of molecules out-of-focus, and high motion blur on images recorded with scanning two-photon microscopy (2PM) in vivo pose a challenge to the accurate localisation of molecules.
A Hybrid Approach to Full-Scale Reconstruction of Renal Arterial Network
The renal vasculature, acting as a resource distribution network, plays an important role in both the physiology and pathophysiology of the kidney.
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis
Recently, attempts have been made to reduce annotation requirements in feature-based self-explanatory models for lung nodule diagnosis.
Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning
Accurate geometry representation is essential in developing finite element models.
Reducing Annotation Need in Self-Explanatory Models for Lung Nodule Diagnosis
Visualisation of the learned space further indicates that the correlation between the clustering of malignancy and nodule attributes coincides with clinical knowledge.
Revisiting Transformer-based Models for Long Document Classification
The recent literature in text classification is biased towards short text sequences (e. g., sentences or paragraphs).
A direct geometry processing cartilage generation method using segmented bone models from datasets with poor cartilage visibility
We present a method to generate subject-specific cartilage for the hip joint.
First Order Locally Orderless Registration
First Order Locally Orderless Registration (FLOR) is a scale-space framework for image density estimation used for defining image similarity, mainly for Image Registration.
RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy
Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data.
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
We propose U-Time, a fully feed-forward deep learning approach to physiological time series segmentation developed for the analysis of sleep data.
Information-Theoretic Registration with Explicit Reorientation of Diffusion-Weighted Images
We present an information-theoretic approach to the registration of images with directional information, and especially for diffusion-Weighted Images (DWI), with explicit optimization over the directional scale.
An Average of the Human Ear Canal: Recovering Acoustical Properties via Shape Analysis
This is however not the case for the part of the ear canal that is embedded in the skull, until the typanic membrane.
A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images
Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population variability.
