Digital Pathology Patents

Digital pathology patents cover whole slide imaging WSI scanner optical design and focus map innovations; slide management high-throughput automated loading and imaging innovations; image management LIMS integration DICOM WSI storage and viewer innovations; and computational pathology AI model training and inference pipeline innovations — with IP held by scanner OEMs, image analysis software companies, and AI diagnostics startups: MAJOR DIGITAL PATHOLOGY PATENT HOLDERS: LEICA BIOSYSTEMS/APERIO: 500+; specific scanner innovations (specific specific Aperio AT2: specific specific 40x 0.25 μm/pixel from specific specific 15 seconds per slide from specific specific Z-stack 25 focal planes from specific specific automated focus map 400 regions from specific specific GigE Vision Sony IMX from specific specific TIFF SVS format from specific specific NDPI NanoZoomer format from specific specific JPEG2000 lossless compression from specific specific Aperio eSlide Manager: specific specific pathology LIS LIMS integration from specific specific HL7 FHIR API from specific specific web-based viewer from specific specific Aperio CS2: specific specific brightfield fluorescence from specific specific 20x 40x objective from specific specific FITC TRITC DAPI ALEXA from specific specific 8-channel fluorescence from specific specific Leica Biosystems UK); PHILIPS: 300+; specific IntelliSite innovations (specific specific IntelliSite UFS scanner: specific specific 40x objective 0.25 μm/pixel from specific specific 60 seconds per slide from specific specific brightfield fluorescence from specific specific 3D Z-stack from specific specific DICOM WSI 145 from specific specific WADO-RS RESTful from specific specific IMS image management from specific specific 99.5% uptime SLA from specific specific CE IVDR 2017/746 from specific specific FDA 510k clearance from specific specific IntelliSite Pathologist Hub); HAMAMATSU: 400+; specific NanoZoomer innovations (specific specific NanoZoomer S360: specific specific 40x 0.23 μm/pixel from specific specific 240 slides/hr from specific specific fluorescence H&E IHC from specific specific NDPI tiff lossless from specific specific NDP.view2 viewer from specific specific NDP.serve image server from specific specific NanoZoomer S60 S80 S360 S80MD); 3DHISTECH: 200+; SECTRA: 200+.

AI computational pathology deep learning convolutional neural network H&E slide classification and cell detection innovations; stain normalization and domain adaptation Macenko Vahadane Reinhard method innovations; multiple instance learning MIL weakly supervised WSI slide-level prediction innovations; and self-supervised learning foundation model pretraining on pathology slide innovations represent core digital pathology patent domains: AI PATHOLOGY PATENTS: PAIGE AI; PATHAI; AIFORIA; DEFINIENS: specific AI innovations (specific specific Paige Prostate cAImeleon: specific specific ResNet-50 backbone from specific specific H&E slide 0.5 μm/pixel from specific specific 512×512 patch 256×256 stride from specific specific gleason grade 3+3 3+4 4+3 from specific specific sensitivity 95.5% specificity 97.5% from specific specific FDA 510k 2021 De Novo from specific specific first AI pathology clearance from specific specific Paige Breast from specific specific Paige Cervix from specific specific PathAI: specific specific MIT-derived from specific specific ResNet BERT histopathology from specific specific clinical trial pathology review from specific specific 14B parameter from specific specific multiple instance learning: specific specific ABMIL attention MIL from specific specific TransMIL Transformer from specific specific 8,192 patches/slide from specific specific weakly-supervised slide-label from specific specific no pixel annotation from specific specific slide-level AUC 0.98 TCGA LUAD from specific specific CLAM clustering MIL from specific specific UNI foundation model: specific specific ViT-L patch 16 16×16 μm from specific specific 100K WSI pretraining from specific specific 100M parameters from specific specific CONCH multi-modal from specific specific self-supervised DINO CTransPath PLIP from specific specific cell detection: specific specific HoVer-Net instance segmentation from specific specific StarDist polar distance from specific specific CellViT ViT foundation from specific specific 3 cells/μm² density from specific specific centroid x y class type from specific specific mitosis: specific specific MIDOG 2022 benchmark from specific specific F1 0.85 from specific specific H-score TIL density); STAIN NORMALIZATION PATENTS: MACENKO; VAHADANE; OPENAI; ILLUMINA: specific stain normalization innovations (specific specific Macenko SVD: specific specific H matrix stain vectors from specific specific singular value decomposition from specific specific normalize to reference from specific specific Vahadane sparse NMF: specific specific dictionary learning OD from specific specific 0.03 Δ color from specific specific Reinhard: specific specific LAB color space from specific specific channel mean std transfer from specific specific CycleGAN stain transfer: specific specific unpaired image translation from specific specific generator discriminator from specific specific perceptual loss from specific specific SCI FID from specific specific StainTools Python from specific specific CycleGAN pytorch from specific specific domain adaptation: specific specific DANN domain adversarial from specific specific multi-site stain variation from specific specific scanner-to-scanner from specific specific Stanford Brigham Mayo from specific specific batch correction from specific specific ComBat parametric from specific specific virtual staining: specific specific label-free → H&E from specific specific brightfield fluorescence from specific specific pix2pix U-Net from specific specific SSIM 0.92 PSNR 32 dB from specific specific Lunit SCOPE IO: specific specific TIL quantification from specific specific tumor vs stroma from specific specific 12 cancer types from specific specific regulatory: specific specific FDA IMDRF SaMD from specific specific IVDR 2017/746 Article 10 from specific specific DP-DX companion Dx from specific specific LDT vs. IVD); MOLECULAR PATHOLOGY PATENTS: FOUNDATION MEDICINE; ILLUMINA; VERACYTE: specific molecular innovations (specific specific FISH-AI: specific specific FISH probe CNN from specific specific ALK ERBB2 gene amp from specific specific spatial transcriptomics: specific specific 10x Genomics Visium from specific specific spot 55 μm pitch from specific specific 3,000-5,000 genes/spot from specific specific Xenium cell 200nm from specific specific Stereo-seq 0.5 μm from specific specific HER2 scoring: specific specific HercepTest 0/1+/2+/3+ from specific specific PathAI CISH AI from specific specific IHC 3+ 10% cells 3+ from specific specific TMB MSI: specific specific FoundationOne CDx from specific specific TMB-H 10 mut/Mb from specific specific MLH1 MSH2 PMS2 IHC).

DICOM whole slide imaging DICOM WG26 145 supplement WSI tile retrieval innovations; pathologist annotation workflow active learning and label propagation innovations; and federated learning multi-site training privacy-preserving differential privacy innovations represent additional digital pathology patent domains: DICOM WSI PATENTS: PHILIPS; LEICA; HAMAMATSU; NEMA: specific DICOM innovations (specific specific DICOM WSI: specific specific supplement 145 whole slide from specific specific VL Whole Slide Microscopy Image from specific specific tile 256×256 or 512×512 from specific specific JPEG2000 JPEG lossless from specific specific optical path attributes from specific specific WADO-RS DICOMweb from specific specific resolution pyramid from specific specific 40x 20x 10x 5x from specific specific image viewer: specific specific OpenSeadragon JavaScript from specific specific IIIF International Image from specific specific Orthanc open-source DICOM from specific specific Pathomation QuPath from specific specific PACS integration: specific specific Epic Cerner Sectra LIS from specific specific HL7 v2.x v3 FHIR from specific specific ADT ORM ORU from specific specific mTLS OAuth 2.0 JWT from specific specific WSI annotation: specific specific ASAP QuPath annotation from specific specific polygon bounding box from specific specific QUIP TCGA annotation from specific specific COCO format JSON from specific specific active learning: specific specific uncertainty sampling from specific specific query-by-committee from specific specific 10× label efficiency from specific specific semi-supervised: specific specific FixMatch pseudo-label from specific specific MixMatch interpolation from specific specific 50 labeled + 5000 unlabeled from specific specific label propagation from specific specific random walk graph from specific specific tissue graph GNN); FEDERATED LEARNING PATENTS: RHINO HEALTH; SUBSTRA; IBM: specific federated innovations (specific specific FL pathology: specific specific FedAvg McMahan 2017 from specific specific 10-50 hospital sites from specific specific gradient compression 10× from specific specific differential privacy ε=1 δ=10⁻⁵ from specific specific DP-SGD Gaussian noise σ from specific specific Rhino FCP federated compute from specific specific Substra permissioned FL from specific specific NVFLARE NVIDIA from specific specific secure aggregation from specific specific HE homomorphic encryption from specific specific multi-site validation: specific specific AUC 0.95 10-site vs. 0.92 single from specific specific site shift scanner stain from specific specific virtual staining: specific specific label-free brightfield → H&E from specific specific CNN encoder decoder from specific specific GAN discriminator from specific specific Orion Ai VSPQ from specific specific Invenio Imaging from specific specific regulatory FL: specific specific FDA AI/ML Action Plan from specific specific locked vs. adaptive SaMD from specific specific predetermined change control plan from specific specific from specific specific Total Product Lifecycle); BIOMARKER DIGITAL PATENTS: ASTRAZENECA; PFIZER; ROCHE: specific biomarker innovations (specific specific companion diagnostic: specific specific PD-L1 22C3 IHC 28-8 SP142 from specific specific TPS CPS algorithm from specific specific FDA co-approval from specific specific PathAI CPS AI from specific specific HER2 3+ 2+ CISH from specific specific TIL quantification: specific specific TILs TSI TIL% from specific specific TILs Working Group from specific specific ASCO CAP 2023 guideline from specific specific spatial biomarkers: specific specific tumor-stroma interface from specific specific CD8 TIL infiltration from specific specific Spatial TME profiling from specific specific proximity analysis from specific specific HALO quant).

Digital pathology startup IP strategy must navigate Leica Biosystems/Aperio AT2 scanner and Aperio eSlide Manager patents (500+), Philips IntelliSite UFS 40x DICOM WSI and image management patents (300+), Hamamatsu NanoZoomer S360 high-throughput scanner patents (400+), Paige AI FDA 510k prostate cancer detection ResNet H&E classification patents (100+), PathAI MIL weakly-supervised slide-level prediction patents (100+), and Foundation Medicine FoundationOne CDx NGS TMB MSI companion diagnostic patents (200+); understand that Leica Aperio, Philips, and Hamamatsu hold the dominant WSI scanner optical design and image management IP, Paige AI holds the first FDA 510k AI pathology De Novo clearance IP, and Foundation Medicine holds the NGS-based TMB MSI molecular pathology companion diagnostic IP; identify whitespace in novel self-supervised foundation model for pathology (ViT-L 100K WSI pretraining, 100M parameter; UNI CONCH open research), novel virtual staining label-free brightfield to H&E CycleGAN (SSIM 0.92), novel spatial transcriptomics + WSI fusion (10x Xenium + H&E multi-modal), and novel federated learning multi-hospital differential privacy pathology AI: DIGITAL PATHOLOGY STARTUP IP STRATEGY: UNDERSTAND THE DIGITAL PATHOLOGY PATENT LANDSCAPE — APERIO PHILIPS HAMAMATSU SCANNER AND PAIGE AI FIRST-CLEARED HOLD BROAD FOUNDATIONAL IP: Leica Aperio AT2 scanner focus map compression format and Philips IntelliSite DICOM WSI image management patents and Paige FDA De Novo 510k prostate cancer AI clearance cover the dominant digital pathology commercial landscape — new entrants need novel hardware (sub-10s 40x scanner, 3D Z-stack z-depth tissue, label-free fluorescence), novel AI architecture (self-supervised foundation model, MIL with spatial attention, multi-modal spatial transcriptomics), or novel regulatory pathway (novel CDx companion Dx co-approval for novel target therapy); NOVEL AI FOUNDATION MODEL FOR PATHOLOGY AND SPATIAL TRANSCRIPTOMICS FUSION ARE HIGHEST-VALUE LEAST-CONSOLIDATED IP: After Paige ResNet prostate 510k and PathAI MIL ABMIL TCGA, novel pathology-specific ViT foundation model (UNI CONCH open research → commercial patent), novel spatial transcriptomics gene expression map integrated with WSI morphology prediction (10x Xenium + H&E multi-modal CNN), and novel virtual staining (brightfield to H&E IHC FISH without staining reagents) represent commercially validated patent whitespace; COMPANION DIAGNOSTIC CDX REGULATORY PATHWAY CREATES HIGHEST-VALUE CLINICAL PATHOLOGY IP: FDA-approved companion diagnostic requires PMA IVD submission and clinical trial validation — novel AI-predicted biomarker proxy (AI-predicted TMB from H&E without sequencing, AI-predicted MSI from H&E Kather et al. Nature Medicine 2019 AUC 0.97) represents commercializable clinical IP that is patentable as novel diagnostic method; WHEN TO PATENT IN DIGITAL PATHOLOGY AI: NOVEL AI MODEL WITH MEASURED AUC, SENSITIVITY, SPECIFICITY VS. PATHOLOGIST BENCHMARK: specific novel pathology AI model (specific specific model architecture + specific specific training data slides + specific specific task + specific specific AUC sensitivity specificity vs. pathologist) vs. specific Paige Prostate 510k sensitivity 95.5% specificity 97.5% ResNet H&E 40x or specific UNI ViT-L 100K WSI AUC 0.98 TCGA LUAD slide-level baseline — measured AUC, sensitivity %, specificity %, inter-observer agreement κ, and multi-site generalization vs. FDA-cleared Paige or TCGA benchmark is the critical digital pathology IP metric; KEY FTO CHECKLIST: Aperio AT2 40x 0.25μm 15s Z-stack SVS TIFF GigE; Philips IntelliSite 40x 60s DICOM WSI WADO-RS CE IVDR; Hamamatsu NanoZoomer S360 240 slides/hr NDPI; Paige cAImeleon ResNet 510k 2021 prostate H&E; PathAI MIL ABMIL TransMIL; UNI ViT-L CONCH DINO CTransPath self-supervised; HoVer-Net StarDist CellViT cell detection; Macenko Vahadane CycleGAN stain normalization; DICOM Supp145 WSI tile JPEG2000 WADO-RS HL7 FHIR; Rhino FCP FL DP-SGD ε=1; FedAvg McMahan gradient compression; 10x Visium Xenium spatial transcriptomics; TMB-H 10 mut/Mb MSI-H MLH1 MSH2 IHC PD-L1 22C3 28-8.