THE RESEARCH STORY
Begin with the decision—not the model.
Pap-smear review requires specialist attention across large numbers of cells. This proposal studies an assistive model that does not treat imagery only as a black box, but also calculates interpretable morphological evidence such as nucleus and cytoplasm boundaries and the N/C ratio.
Health AI becomes useful when it clarifies evidence rather than merely producing an impressive score. The proposal therefore connects cell segmentation and the N/C ratio to outputs specialists can inspect.
PRIMARY RESEARCH QUESTION
What must the study resolve?
Can combining image-based representations with morphological features improve accuracy, interpretability and cross-dataset robustness in cervical-cell classification?
OBJECTIVES
- 01
Develop instance segmentation of nuclei and cytoplasm
- 02
Extract interpretable N/C ratio and morphological features
- 03
Study image–morphology fusion and evaluate cross-dataset robustness
METHOD ARCHITECTURE
From input to accountable support
The method is organized as an evidence chain. Every transformation should preserve enough context to explain what entered the system, what the model inferred, how confidence was assessed and when a person must review the case.
- 01
Cell-image preparation and quality control
- 02
Instance segmentation of nucleus and cytoplasm
- 03
N/C ratio and morphological feature extraction
- 04
Fusion with deep image features
- 05
Specialist and external-dataset evaluation
WORK PACKAGES
Four connected research responsibilities
Segmentation
Develop nucleus/cytoplasm instance segmentation and measure boundary error.
Feature fusion
Combine learned image representations with auditable morphological features.
Validation
Use data made available by the National Cancer Institute and the public SIPaKMeD dataset subject to relevant permissions.
Clinical usefulness
Evaluate sensitivity, specificity, calibration, subgroup performance and error patterns requiring escalation.
EVIDENCE PLAN
What the project should produce
A research project is stronger when its outputs can be inspected independently of the final model score. The dossier therefore separates artifacts from evaluation.
Expected research assets
- Nucleus and cytoplasm instance-segmentation prototype
- Defined and auditable morphological feature set
- Fusion model combining image and morphological representations
- Cross-dataset and subgroup robustness report
- Research pathway for specialist-supervised screening support
Evaluation dimensions
- Dice/IoU and boundary accuracy
- Sensitivity and specificity
- Precision, recall and clinically relevant error analysis
- Calibration and confidence intervals
- External-dataset and subgroup performance
RESEARCH TRANSLATION
From prototype to a credible working system
Translation requires collaboration with pathologists, cytotechnologists, hospitals, screening services, data custodians and regulators from question design through validation. A research prototype must not enter clinical use before appropriate approval.
LIMITATIONS AND RESPONSIBLE FRAMING
The boundary is part of the research design.
This is a research proposal, not an approved diagnostic device. Model output must not replace slide interpretation or diagnosis by qualified healthcare professionals and requires research ethics, data protection and clinical validation.
This dossier communicates the documented project design and intended evaluation. It does not convert a proposal, funded status or prototype into a claim of validated operational performance. Numerical results should be cited only from a resulting peer-reviewed publication or verified project report.
PROJECT RECORD AND CITATION
A stable record for this research direction
FORMAL PROJECT TITLE
Development of an Advanced Artificial Intelligence Model using Morphological Features to Enhance Cervical Cancer Screening EfficiencySuggested citation for this public dossier:
Arayaphan, W. (2027). Development of an Advanced Artificial Intelligence Model using Morphological Features to Enhance Cervical Cancer Screening Efficiency. Research project dossier, Warin Arayaphan.Use the original project report or resulting publication when citing methods, data or findings.STUDENT PATHWAYS
Where students can enter the research
Student directions include biomedical segmentation, morphology extraction, multimodal fusion, calibration, explainable AI, dataset shift and responsible health AI.
We can shape it into a dataset, experiment, model, system component or responsible deployment study.