Model Agnostic Preference Optimization for Medical Image Segmentation

• URL: http://arxiv.org/abs/2512.15009v1
• Date: Wed, 17 Dec 2025 01:50:52 GMT
• Title: Model Agnostic Preference Optimization for Medical Image Segmentation
• Authors: Yunseong Nam, Jiwon Jang, Dongkyu Won, Sang Hyun Park, Soopil Kim,
• Abstract summary: Preference optimization offers a scalable supervision paradigm based on relative preference signals.<n>We propose MAPO (Model-A Preference Optimization), a training framework that utilizes Dropout-driven segmentation hypotheses.<n> MAPO is fully dimensionality-agnostic, supporting 2D/3D CNN and Transformer-based segmentation pipelines.
• Score: 5.289507655906182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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