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Found 9 papers, 6 papers with code
UniverSeg: Universal Medical Image Segmentation
We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training.
Label conditioned segmentation
For segmentation tasks with multiple classes, the standard approach is to use a network that computes a multi-channel probabilistic segmentation map, with each channel representing one class.
Hyper-Convolutions via Implicit Kernels for Medical Imaging
The key building block of a CNN is the convolutional kernel that aggregates information from the pixel neighborhood and shares weights across all pixels.
Hyper-Convolution Networks for Biomedical Image Segmentation
In this paper, we propose a powerful novel building block, the hyper-convolution, which implicitly represents the convolution kernel as a function of kernel coordinates.
Geodesic random walks, diffusion processes and Brownian motion on Finsler manifolds
We show that geodesic random walks on a complete Finsler manifold of bounded geometry converge to a diffusion process which is, up to a drift, the Brownian motion corresponding to a Riemannian metric.
Differential Geometry Analysis of PDEs Probability
Ensembling Low Precision Models for Binary Biomedical Image Segmentation
Our core idea is straightforward: A diverse ensemble of low precision and high recall models are likely to make different false positive errors (classifying background as foreground in different parts of the image), but the true positives will tend to be consistent.
Volumetric landmark detection with a multi-scale translation equivariant neural network
Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection.
Volumetric landmark detection with a multi-scale shift equivariant neural network
Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection.
