All Competitors

Geometric deep learning model that learns universal atomic-scale representations of intermolecular interfaces across proteins, small molecules, ions, lipids, and nucleic acids.

Fifth-place solution from the CZII CryoET Kaggle competition; an ensemble of four lightweight 3D U-Nets for protein particle localization in cryo-ET tomograms.

First-place solution from the CZII CryoET Object Identification Kaggle competition; an ensemble of 3D EfficientNet-encoder U-Nets for multi-class protein particle picking.

Seventh-place CZII CryoET Kaggle solution; an ensemble of three heatmap-predicting 3D segmentation models using ResNet50d and EfficientNetV2-M backbones for particle picking.

Eighth-place CZII CryoET Kaggle solution; a weighted model soup of tiny, medium, and large 3D U-Nets pretrained on simulated data and fine-tuned on experimental cryo-ET tomograms.

A SAM2-based deep learning framework for vesicle segmentation in cryo-ET tomograms and 2D micrographs, supporting both zero-shot and fine-tuned inference.

A deep learning framework for multi-class protein particle picking in cryo-ET tomograms using a 3D U-Net with automated architecture search via Bayesian optimization.

A variational autoencoder for interpretable 3D reconstruction and representation learning of protein subtomograms from cryo-ET data, trained on 5.8 million synthetic particles.

A differentiable wave-optical framework for label-agnostic computational microscopy of biomolecular density and orientation across diverse imaging modalities.

Foundation model pre-trained on 65M cryo-EM particle images via contrastive learning, enabling zero-shot classification, pose clustering, and quality assessment.