Clinical AI algorithm evaluation — Qanti
Expert evaluation and rating of AI-generated IHC quantification against pathologist reference interpretations, with discrepancy analysis and feedback.
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Clinical diagnostic ground-truth generation and biomarker labeling. Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Qanti®. Education includes Diploma, Thai Board of Anatomical Pathology, Medical Council of Thailand / Royal College of Pathologists of Thailand (2020) and Doctor of Medicine (MD), Faculty of Medicine, Naresuan University (2015). AI-training focus includes data types such as Image, Medical, and DICOM and labeling workflows including Classification, Evaluation, and Rati
Expert evaluation and rating of AI-generated IHC quantification against pathologist reference interpretations, with discrepancy analysis and feedback.
Criteria-based categorical and ordinal biomarker classification and diagnostic ground-truth generation, including HER2 0/1+/2+/3+, HER2-low/ultralow, PD-L1 TPS/CPS, Ki-67, Claudin18.2, MSI, and pathology diagnoses.
Protocol-driven pathology classification, biomarker interpretation, specimen adequacy review, and structured clinical reporting for trial cases.
ROI/region marking and basic cell detection on whole-slide images.
Digital whole-slide image scoring and classification of Claudin18.2 IHC cases for proficiency testing and external quality assurance.
Diploma, Thai Board of Anatomical Pathology, Anatomical Pathology
Doctor of Medicine (MD), Medicine
Anatomical Pathologist & Digital Pathology Lead
Professional Observation Visit