Medical Imaging
Last updated
Last updated
Medical imaging encompasses a diverse range of techniques that generate 2D representations of internal anatomical structures. These images are essential for:
Disease diagnosis
Monitoring and progression tracking
Treatment planning
Rexis enables secure AI development on imaging data by integrating Equivariant Encryption (EE) into the training and evaluation pipeline.
X-rays are one of the most widely used imaging modalities, especially for assessing bones, fractures, and dense tissues. They produce 2D projectional images using electromagnetic radiation.
Data Support
X-ray images are typically stored in DICOM format, which includes both pixel data and metadata.
Using EE, Rexis enables encrypted storage and processing of chest X-rays, allowing models to be trained and evaluated without accessing raw images, preserving patient privacy.
Model Support
We support secure fine-tuning and inference for X-ray models using EE.
A vision-language model trained on biomedical literature and images; bridges textual and visual representations.
A model that performs automated radiology reporting and disease localization on chest X-rays.
Uses self-supervised learning to detect chest pathologies without requiring extensive annotation.
Pathology images are ultra-high-resolution scans (WSIs) of stained tissue samples used to diagnose disease at the cellular level.
Data Support
Whole Slide Images (WSIs) are often stored in formats like SVS or TIFF. These gigapixel images are sensitive and often contain identifying histopathological features.
Rexis supports EE-protected training and evaluation pipelines to ensure privacy even during high-resolution model development.
Model Support
We support secure fine-tuning of digital pathology models under encrypted settings.
Ophthalmology relies on imaging of the eye’s internal structures, particularly:
Fundus Photography (CFP): color images of the retina
Optical Coherence Tomography (OCT): high-resolution cross-sections of retinal layers
Data Support
Retinal imaging data is typically stored as JPEG, PNG, or TIFF. EE ensures that models trained on retinal data never access or leak raw patient images, allowing safe collaboration across eye hospitals and research labs.
Model Support
We support secure fine-tuning and encrypted inference of ophthalmology models.
Dermatology imaging includes clinical photos and dermoscopy, where magnified skin images reveal fine-grained details used to detect malignancies and other conditions.
Data Support
Skin images are typically stored in JPEG, PNG, or DICOM. EE allows dermatological datasets to be stored and processed securely—enabling privacy-preserving AI development even when using personal or sensitive skin imagery.
Model Support
We support encrypted fine-tuning and secure inference for skin image models.
A pathology foundation model optimized for panoptic segmentation across large histology datasets.
A visual-language model that combines contrastive learning with histopathology image embeddings.
A whole-slide model incorporating provenance to enhance interpretability and auditability in pathology AI.
A foundation model for disease detection from retinal images, trained for generalization across imaging domains.
A unified learning framework aimed at improving robustness and label-efficiency for medical imaging AI.
A dermatology foundation model providing strong visual embeddings. These embeddings reduce the need for large labeled datasets, improving accessibility and training efficiency for skin disease models.
