AI-POWERED HISTOPATHOLOGY
DeepCancer
Your expertise.
Now with an AI perspective.
From breast tissue images to classification. From model attention to visual explanation. Add a new layer of analysis to your pathology research with DeepCancer.
For healthcare professionals · Research and education
- explainable AI
- digital pathology
- breast histopathology
- computational pathology
- pathology AI
- Accuracy
- 98.86%
- Sensitivity
- 99.25%
- Specificity
- 98.09%
WHY DEEPCANCER FOR YOUR TEAM?
See more than a result.
PATHOLOGISTS & PHYSICIANS
Look into the model’s reasoning.
Review benign / malignant classification alongside highlighted image regions. Grad-CAM helps you interrogate research outputs with your own expertise.
CLINICS & LABORATORIES
Bring research to your team.
Image upload, AI analysis and visual interpretation in one web workflow. Explore how it could fit your team’s research and training in a demo.
HOSPITAL LEADERSHIP
See the technology before deciding.
Go beyond a presentation. Explore the working platform, published method and measured results. Define an evaluation scope that fits your institution.
FROM IMAGE TO INSIGHT
Upload. Analyze.
See where it looks.
The hybrid model combines Swin Transformer and ConvNeXt for computational pathology on digital slides. Grad-CAM visualizes the regions that influenced the model’s decision.
- 01
Histopathology image
An RGB breast tissue image of at least 224 × 224 pixels.
- 02
Hybrid AI analysis
Probabilistic benign / malignant classification.
- 03
Expert review
The result and Grad-CAM visualization, together.
The output. And the focus behind it.
An explainable approach to analysisIf you are a healthcare professional, visit DeepCancer.org to try the demo.
MEET THE TECHNOLOGY WITH YOUR TEAM
What could DeepCancer bring to your clinic? Let’s find out.
Explore the image-to-result workflow together. Discuss your research goals and define an evaluation path for your institution.
Request a demo for your clinicWhat stage is DeepCancer at today?
It is a working research prototype that produces classification and Grad-CAM visualizations for breast cancer histopathology images. It is not offered for clinical diagnosis. Independent validation, pilots and required regulatory processes are on the roadmap.
What can we explore in a demo meeting?
We can discuss the prototype’s image-to-analysis workflow, current test results and the scope of a potential research collaboration with your institution.
Can it integrate with our existing systems?
Integration scope is assessed against your infrastructure, data processes and validation needs. No ready-made hospital integration or clinical deployment is promised.
Is DeepCancer digital pathology or computational pathology?
Both. DeepCancer is a research prototype for explainable classification of digital histopathology images. It sits in computational and digital pathology, and it is not a clinical diagnostic device.
DeepCancer is a research and educational tool. It is not FDA/CE approved for clinical diagnosis and is not offered for clinical diagnostic or treatment decisions. Use limitations ↗