牛齿分割研究，对比卷积和注意力模型在非专用ML数据集上的表现，预处理对结果影响大。
该研究对比了卷积和注意力模型在B.O.V.I.D.数据集上的牛齿分割性能。该数据集包含高分辨率牛齿照片及手工制作的分割掩码，并非专为机器学习设计。研究评估了多种预处理和对齐技术来缓解标签不完美问题。尽管预处理对Dice分数和mIoU等定量指标影响有限，但对预测掩码的定性影响显著。
Segmentation of Bovid Dentition Under Imperfect Annotations: A Comparative Study of Convolutional and Attention Models
Semantic segmentation decomposes an image into distinct mask regions corresponding to different object categories, such as people, cars, signs or buildings. Advances in machine learning (ML) have shifted this task away from traditional rule-based heuristics such as edge detection, towards deep neural networks (DNN) that learn to classify pixels directly. However, semantic segmentation DNNs crucially depend on expertly designed mask targets to learn from, and imperfect or misaligned masks can interfere with a model's ability to learn effectively. This paper presents a comparative study of segmentation architectures, ranging from convolutional backbones to vision transformers, applied to the B.O.V.I.D. dataset, a corpus of high-resolution bovid dental photographs paired with hand-made segmentation masks not originally designed for ML-based training. We evaluate a range of preprocessing and alignment techniques to mitigate the resulting label imperfections. We find that while these preprocessing choices have limited effect on quantitative metrics such as Dice score and mIoU, their qualitative impact on predicted masks is substantial.