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Found 15 papers, 7 papers with code
Semi-Implicit Neural Solver for Time-dependent Partial Differential Equations
In this paper, we propose a neural solver to learn an optimal iterative scheme in a data-driven fashion for any class of PDEs.
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset.
clDice - A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy
Novel multimodal imaging methods are capable of generating extensive, super high resolution datasets for preclinical research.
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
In this study, we explore quantitative correlates of qualitative human expert perception.
Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations
Radiomic representations can quantify properties of regions of interest in medical image data.
The MICCAI Hackathon on reproducibility, diversity, and selection of papers at the MICCAI conference
With this growth, however, come new challenges for the community.
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.
A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images
Raster-scan optoacoustic mesoscopy (RSOM) is a powerful, non-invasive optical imaging technique for functional, anatomical, and molecular skin and tissue analysis.
Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRI
Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field.
clDice -- a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
Implicit Neural Solver for Time-dependent Linear PDEs with Convergence Guarantee
The existing implicit schemes are usually iterative and employ a general-purpose solver which may be sub-optimal for a specific class of PDEs.
Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation
Multi-organ segmentation in whole-body computed tomography (CT) is a constant pre-processing step which finds its application in organ-specific image retrieval, radiotherapy planning, and interventional image analysis.
Neural parameters estimation for brain tumor growth modeling
In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans.