Our Services
Medical Deep Learning Services
- Deep learning is transforming healthcare by enabling AI models to interpret complex medical data with unprecedented accuracy. We design and deploy state-of-the-art neural networks tailored for medical applications, including image segmentation, disease classification, anomaly detection, and predictive analytics.
- Our expertise includes CNNs, RNNs, GANs, and transformer-based models, optimized for medical imaging, time-series data, and multimodal datasets. Whether you need real-time AI-assisted diagnostics, automated image processing, or deep learning-powered clinical decision support, we help turn research models into production-ready solutions.
- We work with popular open-source frameworks like TensorFlow, PyTorch, MONAI, and SciKit-Learn, ensuring seamless integration with cloud infrastructure, edge devices, and clinical workflows. From model training and hyperparameter tuning to deploying scalable AI architectures, our team ensures your medical deep learning solution delivers real impact.
Custom deep learning models for medical imaging, predictive analytics, and AI-powered clinical decision support.
Sub-services:
- Medical Image Segmentation & Classification – Advanced deep learning models for detecting and analyzing medical scans.
- Anomaly Detection – AI-powered identification of irregularities in medical imaging and patient data.
- Neural Network Architecture Design – Custom CNNs, RNNs, and transformer models for healthcare applications.
- Hyperparameter Optimization – Fine-tuning deep learning models for maximum accuracy and efficiency.
- Multimodal AI Models – Combining medical images, text, and structured data for comprehensive analysis.
- Self-Supervised & Transfer Learning – Optimizing training efficiency with cutting-edge learning techniques.
- Cloud & Edge Deployment – Scalable AI solutions for real-time medical diagnostics.
- Explainable AI (XAI) for Healthcare – Enhancing model transparency and regulatory compliance.
Case Studies
Real-World Impact
Explore our successful healthcare AI case studies.
Algorithm for Collaboration & Distribution
Updating disparate and unorganized AI algorithm code into cohesive and easily sharable notebooks.
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Clinical UI Design for Radiology AI
Creating intuitive software interfaces that empower doctors and accelerate AI innovation
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Developing 3D Radiology AI Solutions
Moving University Client to the Cloud with Open Source Medical AI Models
Read MorePublications
Explore Our Work
See our 35+ Peer-Reviewed Publications & Patents.
Improved Active Shape Models for Segmentation of the Prostate on MR Imagery
Toth, R. “Improved Active Shape Models for Segmentation of the Prostate on MR Imagery,” M.S. Thesis, Rutgers University, 2010.
Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge
Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., Strand, R., Malmberg, F., Ou, Y., Davatzikos, C., Kirschner, M., Jung, F., Yuan, J., Qiu, W., Gao, Q., Edwards, P.E., Maan, B., van der Heijden, F., Ghose, S., Mitra, J., Dowling, J., Barratt, D., Huisman, H., Madabhushi, A. “Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge.” Medical Image Analysis 25:18(2), Dec 2013 pp. 359-373. doi: 10.1016/j.media.2013.12.002
HistostitcherTM: An informatics software platform for reconstructing whole-mount prostate histology using the extensible imaging platform framework
Toth, R.J., Shih, N., Tomaszewski, J.E., Feldman, M.D., Kutter, O., Yu, D.N., Paulus, J.C., Paladini, G., Madabhushi, A., “HistostitcherTM: An informatics software platform for reconstructing whole-mount prostate histology using the extensible imaging platform framework.” Journal of Pathology Informatics 5(8), Mar 2014. doi: 10.4103/2153-3539.129441
A Domain Constrained Deformable (DoCD) Model for Co-registration of Pre- and Post-Radiated Prostate MRI
Toth, R., Traughber, B., Ellis, R., Kurhanewicz, J., Madabhushi, A., “A Domain Constrained Deformable (DoCD) Model for Co-registration of Pre- and Post-Radiated Prostate MRI.” Neurocomputing 144(20) Nov 2014. pp. 3-12 doi: 10.1016/j.neucom.2014.01.058
Professor Anant Madabhushi
Executive Director for the Emory Empathetic AI for Health Institute
Assistant Professor Andrew Janowczyk
Emory University
Mike Galvin
Co-Founder & Chairman of LivAI
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