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Found 4 papers, 4 papers with code
Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images
Here, we show theoretically that, under assumptions commonly found in microscopy images, OCEs can be learnt through a self-supervised task that predicts the spatial offset between image patches.
Embedding-based Instance Segmentation in Microscopy
Automatic detection and segmentation of objects in 2D and 3D microscopy data is important for countless biomedical applications.
Fully Unsupervised Probabilistic Noise2Void
Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing.
Leveraging Self-supervised Denoising for Image Segmentation
Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images.
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