通过融合分类与分割模型，提升显微镜下食品晶体结块的识别准确率。
Confidence-aware agglomeration classification and segmentation of 2D microscopic food crystal images
- 设计双模型协同框架，结合分类与像素级分割优势。
- 在低置信度标注下仍能准确识别潜在结块实例，准确率显著提升。
- 专为透明水桥导致的难标注问题优化，适合食品质量检测场景。
食品晶体结块现象在结晶过程中发生，会将水分困于晶体之间，影响产品质量。由于水桥透明且单张切片视角有限，人工标注2D显微图像中的结块极为困难。为此，我们首先提出一种监督基线模型，生成粗标签分类数据集的分割伪标签；随后训练一个可同时进行像素级分割的实例分类模型。推理阶段，两模型联合使用以发挥各自在分类与分割上的优势。为保持晶体特性，设计了后处理模块并融入两个步骤。相较于现有方法，本方法在真实阳性结块分类准确率和尺寸分布预测上均有提升。考虑到人工标注置信度的差异，方法在两种置信水平下评估，成功识别潜在结块实例。
原文摘要 · Abstract (English)
Food crystal agglomeration is a phenomenon occurs during crystallization which traps water between crystals and affects food product quality. Manual annotation of agglomeration in 2D microscopic images is particularly difficult due to the transparency of water bonding and the limited perspective focusing on a single slide of the imaged sample. To address this challenge, we first propose a supervised baseline model to generate segmentation pseudo-labels for the coarsely labeled classification dataset. Next, an instance classification model that simultaneously performs pixel-wise segmentation is trained. Both models are used in the inference stage to combine their respective strengths in classification and segmentation. To preserve crystal properties, a post processing module is designed and included to both steps. Our method improves true positive agglomeration classification accuracy and size distribution predictions compared to other existing methods. Given the variability in confidence levels of manual annotations, our proposed method is evaluated under two confidence levels and successfully classifies potential agglomerated instances.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。