W1H-SPQ: Spectral Binary Hashing for Embedded Clinical AI — 5,000 Simultaneous Disease Indexes in 1 GB, Online Learning via Delta-Placement and Interleaved Spectral Injection

Clinical AI systems face a critical infrastructure bottleneck: standard indexes require tens of megabytes per disease class, limiting embedded devices to dozens of simultaneous pathologies. W1H-SPQ spectral binary hashing reduces index memory 110 times compared to FAISS, from 22.1 MB to 0.2 MB per disease class, while maintaining 99.6% mean recall and 99.6% tumor recall across 9 tissue classes on 7,180 NCT-CRC-HE-100K colorectal cancer patches. This enables 5,000 simultaneous disease indexes per gigabyte of RAM versus 45 for FAISS.

The algorithm is hardware and software agnostic: it runs on any architecture (ARM, x86, FPGA) without GPU, without internet connection, on hardware costing as little as 200 dollars. The only domain-specific components are the embedding model and the disease database. Colorectal cancer histopathology is the proof of concept and the hardest case: 9 tissue classes distinguished by subtle cellular architecture differences with no strong color or shape cues. The 99.6% recall is therefore a lower bound for all other medical imaging domains.

Two online learning methods are introduced. Delta-Placement positions new disease vectors at the exact spectral discrimination boundary derived from Grassmannian geometry. Interleaved Spectral Injection injects new disease patches sequentially so each reinforces the spectral signature of its predecessors. Combined, they register a new disease class of 200 patches in 11 seconds with 100% recall and zero cross-contamination. All algorithms are free for public hospitals, NGOs, and health ministries under PolyForm Noncommercial License.