Anole: An Open, Autoregressive and Native Multimodal Models for Interleaved Image-Text Generation

[IEEE TMI-2024] UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation

Segment Any 4D Gausssians

Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

Effortless data labeling with AI support from Segment Anything and other awesome models.

YOLOv10: Real-Time End-to-End Object Detection

Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything, MobileSAM!!

This is the official code for MobileSAM project that makes SAM lightweight for mobile applications and beyond!

A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

Methods to discover gene programs on single-cell data

[CVPR2024] DPHMs: Diffusion Parametric Head Models for Depth-based Tracking

Official implementation of "Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling"

The course introduces the use of open-source large language models (LLMs) from the Hugging Face ecosystem for research in the behavioral and social sciences.

Bioinformatics'2020: BioBERT: a pre-trained biomedical language representation model for biomedical text mining

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

[NeurIPS 2023] AbdomenAtlas 1.0 (5,195 CT volumes plus nine classes)

Data analysis for Stankey CT et al, 2024.

QuPath extension for Segment Anything Model (SAM)

gReLU is a python library to train, interpret, and apply deep learning models to DNA sequences.

Code for the paper "Low Latency Automotive Vision with Event Cameras", published in Nature

A deep-dive on the entire history of deep-learning

Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data

An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).

[MICCAI'23] Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-train

Sparsely Annotated Region and Organ Segmentation (SAROS) - A large, heterogeneous, and sparsely annotated segmentation dataset on CT imaging data

Code used to create the core and extended GBmap, including downstream analyses (cell-cell interactions, spatial transcriptomics deconvolution) and how to produce the figures.

Transparent medical image AI via an image–text foundation model grounded in medical literature

DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image

code for CVPR2024 paper: DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction