All Competitors

A Mamba-based dual-branch UNet that enhances cryo-EM and cryo-ET density maps using local resolution-guided learning to improve interpretability.

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.

Training-free cryo-ET tomogram segmentation that adapts SAM and DINOv2 for 3D volumetric data, enabling full tomogram segmentation from a single user prompt.

Semi-supervised cryo-ET segmentation framework that adapts DINOv2 vision transformers for 3D organelle annotation using sparse 2D slice labels.