VISEM-Tracking
收藏资源简介：
Pre-print and citation: [Pre-print](https://arxiv.org/abs/2212.02842) @article{thambawita2023visem, title={VISEM-Tracking, a human spermatozoa tracking dataset}, author={Thambawita, Vajira and Hicks, Steven A and Stor{\aa}s, Andrea M and Nguyen, Thu and Andersen, Jorunn M and Witczak, Oliwia and Haugen, Trine B and Hammer, Hugo L and Halvorsen, P{\aa}l and Riegler, Michael A}, journal={Scientific Data}, volume={10}, number={1}, pages={1--8}, year={2023}, publisher={Nature Publishing Group} } Motivation and background Manual evaluation of a sperm sample using a microscope is time-consuming and requires costly experts who have extensive training. In addition, the validity of manual sperm analysis becomes unreliable due to limited reproducibility and high inter-personnel variations due to the complexity of tracking, identifying, and counting sperms in fresh samples. The existing computer-aided sperm analyzer systems are not working well enough for application in a real clinical setting due to unreliability caused by the consistency of the semen sample. Therefore, we need to research new methods for automated sperm analysis. Target group The task is of interest to researchers in the areas of machine learning (classification and detection), visual content analysis, and multimodal fusion. Overall, this task is intended to encourage the multimedia community to help improve the health care system through the application of their knowledge and methods to reach the next level of computer and multimedia-assisted diagnosis, detection, and interpretation. Class Label Mapping sperm: 0 cluster: 1 small or pinhead: 2
预印本与引用：[预印本](https://arxiv.org/abs/2212.02842) @article{thambawita2023visem, 标题：《VISEM-Tracking：人类精子追踪数据集》, 作者：Thambawita, Vajira、Hicks, Steven A、Storås, Andrea M、Nguyen, Thu、Andersen, Jorunn M、Witczak, Oliwia、Haugen, Trine B、Hammer, Hugo L、Halvorsen, Pål、Riegler, Michael A, 期刊：《科学数据（Scientific Data）》, 卷：10, 期：1, 页码：1-8, 年份：2023, 出版商：自然出版集团（Nature Publishing Group） } 动机与背景 手动通过显微镜评估精子样本不仅耗时耗力，还需要经过长期系统训练的专业技术人员，人力成本高昂。此外，由于新鲜精液样本中精子的追踪、识别与计数流程极为复杂，人工精子分析存在可重复性差、人员间差异显著的问题，导致其分析结果可靠性大幅降低。现有计算机辅助精子分析系统受精液样本一致性不足的问题影响，可靠性难以达标，无法适配实际临床场景的应用需求。因此，亟需研发自动化精子分析的新型技术方法。 目标群体 本任务面向机器学习（分类与检测方向）、视觉内容分析、多模态融合领域的研究者。总体而言，本任务旨在推动多媒体领域研究者将其知识与技术方法应用于计算机与多媒体辅助诊断、检测及解读的进阶阶段，助力医疗保健体系的完善与升级。 类别标签映射 精子：0 簇团：1 小头/针尖状：2
