Abstract
The degree of embryo fragmentation serves as a critical morphological indicator for assessing embryo developmental potential in In Vitro Fertilization (IVF) clinical decision-making. However, current manual grading processes are not only time-consuming but also limited by significant inter-observer variability and efficiency bottlenecks. Although deep learning has demonstrated potential in automated grading in recent years, existing solutions face a significant challenge: pure regression models lack the visual explainability required for clinical practice, while pure segmentation models struggle to directly translate pixel-level masks into precise clinical grades. This study proposes RegDeepLab, a dual-branch Multi-Task Learning (MTL) framework that integrates State-of-the-Art (SOTA) semantic segmentation (DeepLabV3+) with a multi-scale regression head. Addressing the common issues of "Gradient Conflict" and "Negative Transfer" in multi-task training, we propose a "Two-Stage Decoupled Training Strategy." Experimental results demonstrate that while standard end-to-end MTL training can minimize grading error (MAE=0.046) through our designed "Feature Injection" mechanism, it compromises the integrity of segmentation boundaries. In contrast, our decoupled strategy successfully provides robust and high-precision grading predictions while preserving SOTA-level segmentation accuracy (Dice=0.729). Furthermore, we introduce a "Range Loss" to effectively utilize large-scale discrete grading data for semi-supervised learning. This study ultimately presents a dual-module clinical auxiliary solution that combines high accuracy with visual explainability.
Abstract (translated)
URL
https://arxiv.org/abs/2511.18454
