PatriciaXiao/Conclusion
about Art & Psychology
逗比脑洞青年的狂野实验田。 I am not coding, I am bugging.
about Art & Psychology
TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding (code & data)
Agent skill: deploy a self-hosted Headscale + built-in DERP mesh that stays usable across China's Great Firewall — connect devices in China back to a home base abroad, with a security model that holds even if the VPS is compromised.
Free, high-quality text-to-speech API endpoint to replace OpenAI, Azure, or ElevenLabs
My personal note of installing the OS on an empty machine, with GPU driver
Fighting with ArXiv as a beginner
Circular visualization in Python (Circos Plot, Chord Diagram, Radar Chart)
Sentence Embeddings with BERT & XLNet
An open source implementation of CLIP.
PyTorch code and models for the DINOv2 self-supervised learning method.
OHIF zero-footprint DICOM viewer and oncology specific Lesion Tracker, plus shared extension packages
BiomedCLIP data pipeline
[Nature Medicine'25] PanDerm: A Multimodal Vision Foundation Model for Clinical Dermatology
EchoCLIP hhttps://www.nature.com/articles/s41591-024-02959-y official code
[MICCAI 2022] The official code for "mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation"
This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.
Clean, accessible reproduction of DeepSeek R1-Zero
EchoNet-Dynamic is a deep learning model for assessing cardiac function in echocardiogram videos.
Python suite to construct benchmark machine learning datasets from the MIMIC-III 💊 clinical database.
A Multimodal Transformer: Fusing Clinical Notes With Structured EHR Data for Interpretable In-Hospital Mortality Prediction
Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI. PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.
Topic Modelling for Humans
Source code of our KDD 2022 paper: Predicting Opinion Dynamics via Sociologically-Informed Neural Networks
[NeurIPS 2020] "Graph Contrastive Learning with Augmentations" by Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen