Show HN: Free mammogram analysis tool combining deep learning and vision LLM
17 comments
·May 27, 2025hiatus
You are encouraging people to upload medical data yet you do not use HTTPS. This just feels irresponsible.
coolwulf
Will change it to https as you suggest. Just need to get the ssl certificate.
jph
Excellent work, thank you. Can you do me a favor and contact me about this? I'm joel@joelparkerhenderson.com and I'm working with public sector health care organizations that are seeking examples of AI for radiographic imaging.
coolwulf
I just sent you an email from my personal gmail.
kakoni
Hi! Nice project. Question,"labeled data + radiology report sets", was this something in public domain?
null
potato-peeler
Interesting project. Do you have a write up on how you built this or gh repo?
coolwulf
I will write a blog post of details of the implementation. Will post later on HN. Thanks for the interests.
notme1234
dang Isn't this a repost of this: https://news.ycombinator.com/item?id=31449147 Just added "LLM".
coolwulf
It is the updated version with a newly trained model with more data and added VLLM stage.
null
I've built Neuralrad Mammo AI, a free research tool that combines deep learning object detection with vision language models to analyze mammograms. The goal is to provide researchers and medical professionals with a secondary analysis tool for investigation purposes.
Important Disclaimers: - NOT FDA 510(k) cleared - this is purely for research investigation - Not for clinical diagnosis - results should only be used as a secondary opinion - Completely free - no registration, no payment, no data retention
What it does: 1. Upload a mammogram image (JPEG/PNG) 2. AI identifies potential masses and calcifications 3. Vision LLM provides radiologist-style analysis 4. Interactive viewer with zoom/pan capabilities
You can try it with any mass / calcification mammo images, e.g. by searching Google: mammogram images mass
Key Features: - Detects and classifies masses (benign/malignant) - Identifies calcifications (benign/malignant) - Provides confidence scores and size assessments - Generates detailed analysis using vision LLM - No data storage - images processed and discarded
Use Cases: - Medical research and education - Second opinion for researchers - Algorithm comparison studies - Teaching tool for radiology training - Academic research validation
The implementation details include: 1. 1st stage object detection using PyTorch retinalnet training DDSM+Internal data set 2. 2nd stage fine tuned Qwen2.5 VL with labeled data + radiology report sets 3. Server is implemented with Flask, Client implemented using SvelteJS
The system is designed specifically for research investigation purposes and to complement (never replace) professional medical judgment. I'm hoping this can be useful for the medical AI research community and welcome feedback on the approach.
Address: http://mammo.neuralrad.com:5300