Xibao Li's starred repositories
DeepSeek-VL
DeepSeek-VL: Towards Real-World Vision-Language Understanding
ChuanhuChatGPT
GUI for ChatGPT API and many LLMs. Supports agents, file-based QA, GPT finetuning and query with web search. All with a neat UI.
SubsequentFracturePrediction
Official code repository of "A CT-based Deep Learning Model for Predicting Subsequent Fracture Risk in Patients with Hip Fracture"
DMGN_Survival_Prediction
Deep Multimodal Graph-Based Network for Survival Prediction from Highly Multiplexed Images and Patient Variables
t-SNE-tutorial
A tutorial on the t-SNE learning algorithm
Multi-TransSP
Multi-TransSP for MICCAI2022
cascadia-code
This is a fun, new monospaced font that includes programming ligatures and is designed to enhance the modern look and feel of the Windows Terminal.
ur-cara-net
U-Net modification for object detection in poorly labelled data
tmss_miccai
TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction
tokyonight-vim
A clean, dark vim colorscheme that celebrates the lights of downtown Tokyo at night, based on a VSCode theme by @enkia with the same name [Archived because I'm no longer using this]
tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
TotalSegmentator
Tool for robust segmentation of >100 important anatomical structures in CT and MR images
PlotNeuralNet
Latex code for making neural networks diagrams
Medical-Cross-Modality-Domain-Adaptation
[IJCAI'18] Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss (code&data)
TextBPN-Plus-Plus
Arbitrary Shape Text Detection via Boundary Transformer;The paper at: https://arxiv.org/abs/2205.05320, which has been accepted by IEEE Transactions on Multimedia (T-MM 2023).
BoundaryFormer
Code for CVPR2022 paper: Instance Segmentation with Mask-supervised Polygonal Boundary Transformers
CSDisentanglement_Metrics_Library
This repository constists of the implementations of the Distance Correlation (DC) and Information Over Bias (IOB) metrics proposed in [link]. The two metrics can be used to assess the level of disentanglement between spatial content and vector style representations. Both metrics are ready to use with PyTorch and TensorFlow implementations.