Title: Balancing the False Positive-Negative tradeoff to enhance Image Segmentation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: deep learning, image segmentation, mask transformation, computer vision

Abstract: Image segmentation is a fundamental task in computer vision with applications spanning diverse domains, particularly in medical imaging. However, the effectiveness of segmentation techniques often varies across datasets and tasks. For instance, methods like SRL and cLDice focus on segmenting thin tubular structures, while models such as IC-Net are tailored for brain tumor segmentation in MRI scans. Despite the availability of such specialized approaches, there remains a need for a unified framework that can generalize well across different segmentation challenges. In this study, we work on the observation that in many segmentation tasks, the False Negative Rate (FNR) tends to be significantly higher than the False Positive Rate (FPR). Building on this observation, we propose a novel method, SMM, that enhances segmentation performance by conditioning the ground truth masks during training with the aim to make the model achieve a balance between false positive and false negative rates. Our approach is architecture-agnostic and has been validated on a range of benchmark datasets, consistently outperforming state-of-the-art methods, often achieving significantly better results than the baseline.

TL;DR: The paper aims to enhance image segmentation by balancing false positive and negative values using a dynamic ground truth mask modulation strategy.