All Competitors

Cryo-ET particle picking model, an ensemble of 3D U-Nets with EfficientNet encoders that finds protein complexes in tomograms by heatmap segmentation.

Cryo-ET particle picking model that localizes six protein complexes in tomograms using an ensemble of lightweight 3D U-Nets.

Segment Anything Model finetuned on diverse medical images, giving a reusable promptable checkpoint for interactive and automatic image segmentation.

Codon optimization framework that adds a generative head to a frozen ProtBert protein language model to design highly expressed coding sequences.

Promptable ultrasound image segmentation foundation model, a SAM adaptation trained on US-43d, the largest public ultrasound segmentation corpus.

LoRA fine-tuning framework for ESM-2 with multi-head attention pooling and contact map enhancement for sequence-only protein property prediction.

Masked-autoencoder foundation model that pre-trains a 3D Residual Encoder U-Net on roughly 39,000 brain MRIs for volumetric image segmentation.

Single-cell perturbation prediction model that adds gene-level language embeddings from NCBI, UniProt, and Gene Ontology to scGPT representations.

Protein domain annotation model pairing an ESM-2 backbone with a probabilistic decoder, bringing language-model sensitivity to Pfam-style assignment.

Convolutional ECG foundation model trained on expert annotations spanning 150 diagnostic categories, with 12-lead and single-lead wearable variants.

Chromatin-state language model pretrained on ROADMAP annotations from 127 human cell types to find chromatin-state motifs and predict gene expression.

DeepLabV3 segmentation model that separates tissue from glass background in H&E and IHC whole-slide images, as used by the HEST-Library.

Multimodal model fusing pre-trained DNA, RNA, and protein encoders through cross-attention to predict tissue-specific transcript isoform expression.

3D vision-transformer foundation model for multimodal neuroimage segmentation, pretrained self-supervised on brain MRI from 41,400 participants.

Mixture-of-Experts foundation model for medical image segmentation that generalizes across imaging modalities and clinical centers.

Self-supervised CNN pretrained on 700,000 person-days of UK Biobank accelerometer data for human activity recognition across devices and cohorts.

Self-supervised 3D CT foundation model that extracts general-purpose tumor representations for cancer imaging biomarker discovery and prognosis.

RNA foundation model trained on chemical-mapping data from millions of sequences, predicting reactivity, secondary structure, and degradation.

AlphaFold fine-tuned via OpenFold on 944 high-resolution MHC-peptide structures, reaching median peptide RMSD of 0.65 Å on held-out complexes.

Radiology-specific multimodal LLM that generates the findings section of a chest X-ray report from a frontal image, pairing RAD-DINO with Vicuna-7B.

LoRA and QLoRA fine-tuning of ESM-2 for token-level prediction of protein binding sites and post-translational modification sites from sequence alone.

EEG foundation model that pairs a convolutional encoder with a GPT backbone, pretrained by masked-segment reconstruction for low-data BCI decoding.

Fully 3D promptable segmentation foundation model for volumetric CT and MR, encoding whole volumes so anatomy can be segmented from one prompt point.

Abdominal CT segmentation model driven by CLIP text embeddings, covering 25 organs and 6 tumor types with zero-shot extension to new categories.