---
title: brain-tumor-image-dataset-semantic-segmentation
canonical_url: "https://www.modelscope.cn/datasets/testUser/brain-tumor-image-dataset-semantic-segmentation"
md_url: "https://www.modelscope.cn/datasets/testUser/brain-tumor-image-dataset-semantic-segmentation.md"
repository: testUser/brain-tumor-image-dataset-semantic-segmentation
chinese_name: "脑肿瘤语义分割图像数据集"
last_updated: 2025-04-17
license: "Apache License 2.0"
storage_size: "84 MB"
downloads: 278
stars: 0
---

# brain-tumor-image-dataset-semantic-segmentation

> brain-tumor-image-dataset-semantic-segmentation - testUser 在 ModelScope 开源的数据集。肿瘤分割数据集是专门为TumorSeg计算机视觉项目设计的，该项目专注于语义分割。项目的目标是使用先进技术在医学图像中准确识别肿瘤区域。

testUser/brain-tumor-image-dataset-semantic-segmentation 是 ModelScope 魔搭社区上的数据集，存储大小 84 MB，采用 Apache License 2.0 许可。

- **Repository**: testUser/brain-tumor-image-dataset-semantic-segmentation
- **License**: Apache License 2.0
- **Storage size**: 84 MB
- **Downloads**: 278
- **Stars**: 0
- **Last updated**: 2025-04-17

Source: https://www.modelscope.cn/datasets/testUser/brain-tumor-image-dataset-semantic-segmentation

---

Brain Tumor Image DataSet: Semantic Segmentation
The Tumor Segmentation Dataset is designed specifically for the TumorSeg Computer Vision Project, which focuses on Semantic Segmentation. The project aims to identify tumor regions accurately within Medical Images using advanced techniques.

Details of the Dataset:
Project Type: Semantic Segmentation

Subject: Tumor

Classes:

Tumor (Class 1)
Non-Tumor (Class 0)
Key Information:

Last Updated: 9 months ago
Overview of the Project:
The TumorSeg Computer Vision Project is dedicated to Semantic Segmentation, which involves classifying every pixel in an image as part of a tumor or non-tumor region. This fine-grained approach provides an accurate understanding of the spatial distribution of tumors within medical images.

Classes:
This dataset is divided into two classes:
**Class 0 (Non-Tumor):

Pixels labeled as non-tumor represent areas without any tumor presence in the medical images.
**Class 1 (Tumor):
Pixels labeled as tumors correspond to areas where tumors are detected.
Usage of the Dataset:
Researchers and practitioners in the field of medical image analysis can use this dataset to train and evaluate semantic segmentation models for tumor detection. The binary classification into tumor and non-tumor classes simplifies the segmentation task, making it suitable for various applications.