AI CANCER DIAGNOSIS · PATHOLOGY AI
DeepCancer
Your expertise.
Now with an AI perspective.
Pathology AI for computational and digital pathology: AI-assisted diagnosis and cancer detection from breast histopathology images, with a visual explanation of where the model looked.
For healthcare professionals · Research and education
THE FIELD
AI, cancer, pathology, detection, diagnosis.
DeepCancer sits in this research line: AI cancer diagnosis, then computational pathology and digital pathology, then AI-assisted diagnosis, cancer detection, and pathology AI.
- AI cancer diagnosis
- computational pathology
- digital pathology
- AI-assisted diagnosis
- cancer detection
- pathology AI
- Accuracy
- 98.86%*
- Sensitivity
- 99.25%*
- Specificity
- 98.09%*
* Test results from the published research, not clinical success rates. The model was developed using BreaKHis; performance at other centers requires independent validation.
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 influence AI-assisted diagnosis and cancer detection.
- 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 analysisMEET 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 pathology AI for AI-assisted diagnosis and cancer detection on digital histopathology images — a computational pathology research prototype. 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 ↗