| # Ophthalmic Data Processing, De-identification & Quality Control Toolkit |
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| This toolkit gathers validated, production-tested scripts written by team members for preprocessing, de-identifying, quality filtering, and structuring raw multi-center ophthalmic datasets. |
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| ## 1. Directory Layout & Key Modules |
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| - deidentification/: Privacy redaction & image sanitization |
| - audit_fundus_corner_pii.py: Optical OCR & pixel-density corner PII audit |
| - deidentify_trial.py: Core algorithms (fundus circular mask, frame blackout, OCR regex) |
| - deidentify_batch.py: High-throughput batch de-identification pipeline |
| - fundus_pii_trial.py: Visual inspection harness for burned-in text |
| - test_deidentify_trial.py: Automated regression tests for geometric masking |
| - crop_fundus_images.py: Automatic contour extraction & fundus border cropping |
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| - quality_control/: Multi-stage image & metadata QA |
| - build_quality_risk_review.py: Audits aspect ratios, corrupted headers, and outlier samples |
| - filter_prompt_artifacts.py: Sanitizes prompt formatting noise and deduplicates tags |
| - prompt_qc.py: Validates clinical prompt consistency and anatomical hierarchies |
| - resize_mode_qc.py: Aspect ratio and resolution distribution verification |
| - asrm_preprocess.py: ASRMNet-based fundus quality evaluation preprocessor |
| - clean_excel.py: Cross-references image files against diagnosis tables |
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| - report_parsing/: Clinical report extraction & multimodal matching |
| - materialize_newdata_prompts.py: Generates unified 41-column structured manifests |
| - attach_labels.py: Merges free-text clinical reports with image records |
| - batch_retrieve_reports.py: RoBERTa-SigLIP zero-shot image-to-report cross-modal retrieval |
| - cohorts.py: Master registry of all multi-center hospital datasets |
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| - taxonomy/: Clinical taxonomy & bilingual standardizations |
| - build_mixed_v5_taxonomy.py: Disease category crosswalk mapping |
| - build_mixed_v6_taxonomy.py: 95-disease hierarchical taxonomy builder (ICD/SNOMED aligned) |
| - test_build_mixed_v6_taxonomy.py: Taxonomy integrity tests |
| - test_taxonomy_sampling.py: Category-balanced sampling evaluation |
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| ## 2. Provenance & Attribution |
| Developed by ZJU & Multimodal Ophthalmology Lab contributors (Lisicheng, Zhanghuan, Huchengwei, Lizekun). |
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