临床四联免疫原性给药间隔抗体效力衰减数据集 v0.1

数据集获取与加载（Hugging Face datasets）

# 前置依赖: pip install datasets
from datasets import load_dataset

ds = load_dataset("ClarusC64/clinical-quad-immunogenicity-dosing-interval-antibody-efficacy-decay-v0.1")
print(ds)  # 查看 splits 与字段结构
# 国内网络可先设镜像: import os; os.environ["HF_ENDPOINT"]="https://hf-mirror.com"

git lfs install && git clone https://hf-mirror.com/datasets/ClarusC64/clinical-quad-immunogenicity-dosing-interval-antibody-efficacy-decay-v0.1

数据集大小约 5.9 KB。

仓库文件结构

• .gitattributes
• README.md
• scorer.py

数据说明

Clinical Quad Immunogenicity Dosing Interval Antibody Efficacy Decay v0.1 Each row is a patient snapshot. Core quad ImmunogenicityDosing intervalAntibody titerEfficacy decay Target label_loss_response_next_30d Files data/train.csvdata/tester.csvscorer.py Evaluation Run model on data/tester.csvReturn predictions row alignedScore with scorer.py License MIT

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

完整数据卡：huggingface.co/datasets/ClarusC64/clinical-quad-immunogenicity-dosing-interval-antibody-efficacy-decay-v0.1