Model Details Model Description MorphEm is a self supervised learning framework trained with the DINO Bag of Channels recipe on the entire CHAMMI 75 dataset. It serves as a benchmark for performance for self supervised models. Developed by: Vidit Agrawal, John Peters, Juan Caicedo Shared by: Caicedo Lab Model type: Vision Transformer Small License: MIT License Model Sources Repository: https://github.com/CaicedoLab/CHAMMI 75 Demo: https://github.com/CaicedoLab/CHAMMI 75/tree/main/aws tutorials Uses The model was pre trained with a heterogenous dataset of microscopy images with the goal of obtaining cell morphology embeddings for biological applications. Direct Use The primary use of this model is feature extraction of cellular morphology in image based biological experiments. The model takes single channel images as input and produces feature vectors with discriminative information of cellular phenotypes. The input images should be segmented ahead of time; this model does not identify the location of cells automatically. The feature embeddings have been tested in single cell analysis problems. If the images of interest are multi channel, each channel can be processed independently…
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