Optimizing lung cancer understanding: leveraging active learning for data-efficient AI
Lung cancer remains the leading cause of cancer-related mortality worldwide, yet AI- driven prognoses are often hampered by the high cost and limited availability of richly annotated clinical data. In this thesis, we develop a data-efficient Active Learning frame- work centered on a Multi-Layer Perc...
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| Formato: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/449758 |
| Acesso em linha: | https://hdl.handle.net/2117/449758 |
| Access Level: | acceso abierto |
| Palavra-chave: | Lungs--Cancer Artificial intelligence--Medical applications Active learning Tabular data Medical data Lung cancer Bayesian neural network approximation Pulmons--Càncer Intel·ligència artificial--Aplicacions a la medicina Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Resumo: | Lung cancer remains the leading cause of cancer-related mortality worldwide, yet AI- driven prognoses are often hampered by the high cost and limited availability of richly annotated clinical data. In this thesis, we develop a data-efficient Active Learning frame- work centered on a Multi-Layer Perceptron enhanced with Monte Carlo Dropout to ap- proximate Bayesian uncertainty on structured patient records. Using a retrospective cohort from the Hospital of Barcelona-encompassing demographic, metastatic, labora- tory, histological, and molecular biomarker information-our model begins with a small seed of labeled cases. At each iteration, we perform multiple stochastic forward passes to estimate predictive variance and then query the most uncertain samples for expert anno- tation. We systematically compare class-balancing techniques (SMOTE, ADASYN, and weighted losses) and a variety of sampling strategies-including random, cluster-based, uncertainty-driven, distance-based, and hybrid approaches-across different seed-pool and batch-size configurations. Focused on treatment-response classification, this strategy matches or exceeds the performance of fully supervised models while reducing labeling requirements by up to 60%. We conclude by discussing the trade-offs between annota- tion cost, model confidence, and clinical utility, highlighting Active Learning's potential to accelerate AI adoption in data-constrained healthcare environments. |
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