Medical Computer Vision That Augments Clinical Decision-Making With Visual Intelligence

Our medical computer vision development follows a pipeline-oriented architecture with distinct stages for data preparation, model development, clinical validation, and deployment — each with healthcare-specific requirements that distinguish it from general computer vision work.

Data preparation for medical imaging requires clinical annotation expertise. We work with board-certified specialists (radiologists, pathologists, dermatologists) to create ground truth labels that reflect clinical judgment rather than lay interpretation. Annotation protocols specify the clinical criteria for each label (what constitutes a "positive" finding, what level of certainty constitutes a ground truth label, how borderline cases are handled). We implement multi-reader annotation with adjudication — each image is annotated by at least two clinicians, with disagreements resolved by a third senior clinician, ensuring label quality that reflects clinical consensus rather than individual variation. For DICOM images, we build preprocessing pipelines that handle the unique characteristics of medical imaging: windowing and leveling for CT images, multi-sequence handling for MRI, magnification calibration for pathology slides, and metadata extraction for clinical context.

Model architecture selection is guided by the specific imaging task. For classification tasks (normal vs. abnormal, benign vs. malignant), we use convolutional neural networks (EfficientNet, ConvNeXt) or vision transformers (ViT, Swin Transformer) fine-tuned from models pretrained on medical imaging datasets (RadImageNet, CheXpert) rather than ImageNet, because the visual patterns in medical images differ fundamentally from natural photographs. For detection tasks (localizing findings within an image), we implement architectures optimized for medical imaging (RetinaNet, DETR with domain-specific anchoring). For segmentation tasks (delineating tumor boundaries, measuring wound area), we use U-Net variants (nnU-Net, Swin-UNETR) that have demonstrated state-of-the-art performance on medical image segmentation benchmarks. For whole-slide pathology images, we implement multiple-instance learning (MIL) architectures that process the image in tiles and aggregate predictions, handling the extreme resolution of pathology slides efficiently.

Clinical validation goes beyond standard ML evaluation metrics. We evaluate models for diagnostic accuracy (sensitivity, specificity, positive predictive value, negative predictive value) at clinically meaningful operating points — not just AUC, but performance at thresholds that balance the clinical consequences of false positives and false negatives for the specific use case. We assess performance across patient demographics (age, sex, race), imaging equipment manufacturers (GE, Siemens, Philips, Canon), and acquisition parameters (different kVp settings, contrast administration, slice thickness) to identify subgroup performance variations. For applications targeting FDA clearance, we design validation studies that meet regulatory expectations: sufficient sample size, appropriate clinical comparators, and predefined acceptance criteria.

Deployment integrates with existing clinical imaging infrastructure. We build DICOM-native inference services that receive images from PACS through DICOM routing, process them through the model pipeline, and deliver results back to the clinical workflow — either as structured reports in the radiology worklist, as DICOM Secondary Capture images with AI annotations overlaid on the original image, or as clinical decision support alerts triggered by specific findings. For PACS integration, we support the DICOM standard communication protocols (C-STORE for image receipt, C-FIND for worklist queries) and AI-specific DICOM extensions (DICOM SR for structured reporting, DICOM SEG for segmentation results).

Medical computer vision projects range from $100,000 to $400,000 depending on model complexity, clinical validation requirements, and deployment scope. A focused computer vision application for a single imaging task with PACS integration runs $100,000-$180,000 across 16-24 weeks. Multi-model systems handling multiple imaging modalities or findings, with comprehensive clinical validation and FDA SaMD documentation support, typically fall in the $250,000-$400,000 range across 24-40 weeks. All projects include clinical annotation coordination, HIPAA-compliant training infrastructure, PACS integration, and prospective validation design.