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arXiv · 2512.15009

Model Agnostic Preference Optimization for Medical Image Segmentation

Yunseong Nam·Jiwon Jang·Dongkyu Won·Sang Hyun Park·Soopil Kim
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Abstract

Preference optimization offers a scalable supervision paradigm based on relative preference signals, yet prior attempts in medical image segmentation remain model-specific and rely on low-diversity prediction sampling. In this paper, we propose MAPO (Model-Agnostic Preference Optimization), a training framework that utilizes Dropout-driven stochastic segmentation hypotheses to construct preference-consistent gradients without direct ground-truth supervision. MAPO is fully architecture- and dimensionality-agnostic, supporting 2D/3D CNN and Transformer-based segmentation pipelines. Comprehensive evaluations across diverse medical datasets reveal that MAPO consistently enhances boundary adherence, reduces overfitting, and yields more stable optimization dynamics compared to conventional supervised training.

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Yunseong Nam, Jiwon Jang, Dongkyu Won, Sang Hyun Park, Soopil Kim. 2025-12-17. Model Agnostic Preference Optimization for Medical Image Segmentation. https://arxiv.org/abs/2512.15009

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