All Competitors

Self-supervised 3D pretrained models for CT and MRI that learn anatomical representations from unlabeled volumes and transfer to segmentation tasks.

3D convolutional network that predicts subcellular fluorescence labels from transmitted-light microscopy, enabling label-free imaging of living cells.

Dilated convolutional network that predicts cell-type-specific epigenetic and transcriptional profiles from DNA sequence across mammalian genomes.

Convolutional neural network that predicts DNA accessibility from sequence across 164 DNase-seq cell types, enabling variant effect prediction.