中文标题
基于置信度的2D显微食品晶体图像聚类分类与分割
英文标题
Confidence-aware agglomeration classification and segmentation of 2D microscopic food crystal images
中文摘要
食品晶体团聚是在结晶过程中发生的现象，会将水困在晶体之间并影响食品产品质量。 在二维显微图像中对团聚进行人工标注尤其困难，这是由于水结合的透明性以及仅对成像样品单张切片的有限视角。 为了解决这个挑战，我们首先提出一个监督基线模型，以生成粗略标记分类数据集的分割伪标签。 接下来，训练一个实例分类模型，该模型同时执行像素级分割。 这两个模型在推理阶段被使用，以结合它们在分类和分割方面的各自优势。 为了保留晶体特性，设计了一个后处理模块，并将其包含在两个步骤中。 与现有其他方法相比，我们的方法提高了真正的团聚分类准确性和大小分布预测。 考虑到人工标注置信度水平的差异，我们提出的的方法在两种置信度水平下进行了评估，并成功分类了潜在的团聚实例。
英文摘要
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.
文章页面
[2507.23206] 基于置信度的2D显微食品晶体图像聚类分类与分割