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Found 5 papers, 3 papers with code
CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning
Motivation: In recent years, image-based biological assays have steadily become high-throughput, sparking a need for fast automated methods to extract biologically-meaningful information from hundreds of thousands of images.
Ranked #1 on
BBBC021 NSC Accuracy
on CytoImageNet
Random Embeddings and Linear Regression can Predict Protein Function
Large self-supervised models pretrained on millions of protein sequences have recently gained popularity in generating embeddings of protein sequences for protein function prediction.
W-Cell-Net: Multi-frame Interpolation of Cellular Microscopy Videos
We propose a fully convolutional autoencoder network that takes as input two images and generates upto seven intermediate images.
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers
Understanding if classifiers generalize to out-of-sample datasets is a central problem in machine learning.
Towards a theoretical understanding of false positives in DNA motif finding
Here, however, we derive the theoretical dependence of false positives on dataset size and find that false positives can arise as a result of large dataset size, irrespective of the algorithm used.
