Dr.  Jie  Li
Taylor’s University,  Malaysia

Title: Executive Keynote Briefing: Computational Oncology & Quantum Machine Learning

Abstract:

Context & Overview: This executive summary condenses the 9-slide keynote presentation prepared for the 6th International Conference on Cancer Therapy and Translational Medicine (CTTM 2026, Kunming). It bridges Dr. Li Jie's published research in continuous data cleaning and lightweight convolutional neural networks (CNNs) with emerging hybrid quantum-classical frameworks in digital histopathology and radiation oncology.

1. Institutional Research Foundation & Data Quality Bottlenecks

• Taylor's Impact Lab Research Pillars: The Digital Health & Medical Advancement Impact Lab spans four core thrusts: (1) Diagnostic AI & 3D intensity imaging for stainless histopathology; (2) Mathematics in continuous data cleaning; (3) Smart IoT/UAV medicine delivery; and (4) Open benchmark datasets on Harvard Dataverse.
• Clinical Histopathology Noise: Digital whole-slide colon histopathology exhibits >90% non-biological feature noise caused by microtome tearing, illumination variations, and inter-hospital H&E staining drifts. Standard one-time preprocessing fails; iterative continuous cleaning (ICRAIE 2025) and statistical verification are mathematically proven prerequisites to prevent deep models from memorizing laboratory preparation artifacts.

2. Classical Deep Learning: Architecture Efficiency vs. Overfitting

A systematic evaluation of CNN architectures (Digital Health 2026; Frontiers in Oncology 2025) demonstrates that heavyweight models (ResNet-50, DenseNet, 25M–50M+ parameters) face severe over-parameterization risks and inference latencies in digital pathology scanners. Tailored lightweight CNN architectures achieve clinical-grade accuracy while slashing computational overhead, enabling seamless deployment on point-of-care hospital edge workstations.

3. Quantum Machine Learning Benchmarks & Clinical Translation

*Binary classification benchmark on colorectal adenocarcinoma histopathology (Shahjalal et al., arXiv 2025).
• Real-World Radiation Oncology Case Study (USC ISI & Keck Medicine): Translational collaboration demonstrated that QuFeX (Quantum Feature Extraction + U-Net) reduces model parameters by 6× (250K vs. 1.5M) while boosting organ boundary segmentation accuracy by +7%. This capability accelerates contouring workflows from multiple days to a single clinic visit, enabling daily adaptive radiation therapy.
• Empirical Rigor & 50-Qubit Threshold (Cascade Quantum 2026): Rigorous evaluations across WDBC, TCGA, and MedMNIST indicate no quantum advantage on low-dimensional data (Q_dataset < 50 qubits), as AutoML classical baselines match quantum parity. Genuine quantum utility requires high-dimensional multi-omics, 3D volumetric scans, and hardware error suppression.
4. Four-Stage Translational Roadmap & Concluding Takeaway
The keynote closes with a structured 4-phase clinical translation pathway: (1) Standardized continuous cleaning to secure authentic input baselines; (2) Hybrid quantum-classical co-design coupling lightweight CNNs with 4-to-16 qubit variational circuits; (3) Noise mitigation & error extrapolation on physical NISQ processors; and (4) Multi-center prospective clinical trials for biopsy triaging and radiation planning.

Conclusion: Advancing computational oncology from laboratory experimentation to clinical deployment demands combining rigorous, continuous data engineering with parameter-efficient hybrid architectures. As high-dimensional omics and spatial imaging mature, hybrid quantum-classical systems present a transformative frontier for personalized, ultra-precise cancer diagnostics.

Biography: