Advanced Al for
Breast Cancer Detection
Advanced Imaging. Smart Inference. Clinically Aligned.
Led by Prof. Amit Sethi
we deliver end-to-end breast cancer diagnostics powered by state-of-the-art AI and deep clinical insights. Under the guidance of Prof. Amit Sethi — a global leader in computational pathology — our solutions accelerate detection, improve outcomes, and bring diagnostic equity to resource-limited settings.
Our Solutions
HER2 Status Prediction from H&E
We predict HER2 expression directly from standard H&E slides, eliminating the need for costly and time-consuming confirmatory tests like FISH or IHC in many cases. Validated on TCGA datasets as well as challenging equivocal cases, our models achieve an AUC above 0.85, helping clinicians triage HER2 patients faster and more reliably.
AI Histopathology Analysis
Using weakly-supervised deep learning, our platform enables slide-level classification that highlights malignant regions through intuitive visual heatmaps. It differentiates between invasive and in-situ carcinoma, empowering pathologists to scale their expertise across geographies with greater consistency and efficiency.
Ultrasound-to-Mammogram Enhancement
With a GAN-based approach, we enhance portable ultrasound images to achieve mammogram-like clarity in real time. This technology requires no additional hardware upgrades and makes screening-grade imaging accessible even in under-resourced clinics, bringing early detection capabilities to communities that need them most.
Services We Offer
Solution Licensing
Ready-to-deploy AI pipelines for labs, hospitals, and diagnostic centers
Custom Model Training
Adapt AI to local datasets and clinical settings
Cloud-Based Image Analysis API
Access AI classification and HER2 inference via secure cloud endpoints
Clinical Validation & Research
Collaborate on studies to test AI impact in local or global trials
Consulting & Integration
Workflow design, compliance consulting, and infrastructure support
Deployment-Ready
AI tools ready for immediate clinical use
Flexible Integration
Rapid turnaround and adaptable workflows
Clinical Standards
Aligned with evolving biomarkers and standards
Designed for Clinical Agility
Leadership Team
About Prof. Amit Sethi
Prof. Amit Sethi is a leading expert in computational pathology and deep learning, serving as Full Professor at IIT Bombay and Adjunct Instructor at University of Illinois, Chicago.
Key Achievements
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ICIAR 2018: ~93% accuracy in breast cancer classification
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HER2 prediction from H&E slides (~AUC 0.85)
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Histographs for tissue structure analysis (SPIE 2020)
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GAN-based ultrasound-to-mammogram enhancement