aneurysm-detector

v0.1.0 safe
3.0
Low Risk

基于 CenterNet3D 的颅内动脉瘤检测推理库 — 输入 DICOM 序列，输出 14 类血管位置概率向量。

🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe for use given its low risk scores across all categories except metadata, where the repository could not be located and the maintainer has limited history.

  • No network or shell risks detected
  • Low obfuscation risk with benign context
  • Repository not found and maintainer lacks history
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on local processing like aneurysm detection.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands, which aligns with typical package behavior for medical image analysis.
  • Obfuscation: The obfuscation pattern detected appears to be part of a code snippet initializing a model for an aneurysm detection system, not indicative of malicious intent.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The repository is not found, and the maintainer seems to be new with limited history.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1241 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 12 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • = model.to(device) model.eval() CENTER_NET_MODEL = model # 初始化 transform CENT
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "4c-2026 Team" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aneurysm-detector 
Develop a web-based medical imaging analysis tool using the 'aneurysm-detector' Python package. This tool will enable neurosurgeons and radiologists to upload DICOM image sequences of brain scans and receive real-time feedback on the presence and probability of 14 different types of vascular anomalies, including aneurysms. The application should have the following features:

1. User-friendly interface for uploading DICOM files.
2. Automatic detection of 14 types of vascular anomalies with probability scores.
3. Visualization of detected anomalies on the uploaded images.
4. Detailed report generation summarizing findings, including location, size, and probability scores.
5. Integration with common DICOM viewers for better visual interpretation.
6. Option to save reports and images for future reference.

The 'aneurysm-detector' package will be utilized to process the DICOM files and perform the detection task. Specifically, users will upload their DICOM files through the web interface, which will then be processed by the 'aneurysm-detector' package to detect any vascular anomalies present in the images. The results will be displayed back to the user in a visually intuitive manner, highlighting the detected anomalies and providing detailed information about each one. This tool aims to streamline the diagnostic process and improve accuracy in identifying potential life-threatening conditions such as cerebral aneurysms.

💬 Discussion Feed

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