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Found 5 papers, 3 papers with code
ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images
Unlike color photography images, which are consistently encoded into RGB channels, biological images encompass various modalities, where the type of microscopy and the meaning of each channel varies with each experiment.
Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors
This allows us to accurately retrieve images even from large-scale databases like LAION-5B.
No Free Lunch in Self Supervised Representation Learning
In this work, we explore this relationship, its impact on a domain other than natural images, and show that designing the transformations can be viewed as a form of supervision.
Comparison of semi-supervised learning methods for High Content Screening quality control
In this work, we evaluate deep learning options that do not require extensive image annotations to provide a straightforward and easy to use semi-supervised learning solution to this issue.
aura-net : robust segmentation of phase-contrast microscopy images with few annotations
We present AURA-net, a convolutional neural network (CNN) for the segmentation of phase-contrast microscopy images.
