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一个基础模型可用于跨九种模式的生物医学物体的联合分割和检测及识别—小柯机器人—科学网
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Holistic image analysis comprises interdependent subtasks such as segmentation, detection and recognition, which are tackled separately by traditional approaches. Here, we propose BiomedParse, a biomedical foundation model that can jointly conduct segmentation, detection and recognition across nine imaging modalities. This joint learning improves the accuracy for individual tasks and enables new applications such as segmenting all relevant objects in an image through a textual description. To train BiomedParse, we created a large dataset comprising over 6 million triples of image, segmentation mask and textual description by leveraging natural language labels or descriptions accompanying existing datasets. We showed that BiomedParse outperformed existing methods on image segmentation across nine imaging modalities, with larger improvement on objects with irregular shapes. We further showed that BiomedParse can simultaneously segment and label all objects in an image. In summary, BiomedParse is an all-in-one tool for biomedical image analysis on all major image modalities, paving the path for efficient and accurate image-based biomedical discovery.</p> <p> DOI: 10.1038/s41592-024-02499-w</p> <p> Source: <a href="https://www.nature.com/articles/s41592-024-02499-w">https://www.nature.com/articles/s41592-024-02499-w</a></p> </p> <div class="remarks">期刊信息</div> <div class="dd"><p> <span style="font-weight: bold;">Nature Methods：</span>《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99<br /> <span style="font-weight: bold;">官方网址：</span><a href="https://www.nature.com/nmeth/" target="_blank">https://www.nature.com/nmeth/</a><br /> <span style="font-weight: bold;">投稿链接：</span><a href="https://mts-nmeth.nature.com/cgi-bin/main.plex" target="_blank">https://mts-nmeth.nature.com/cgi-bin/main.plex</a></p> </div> </div> </div> <style> #footer{ background:#890f0e; text-align:center; padding:10px; color:#FFF; font-size:12px;} #footer a{color:#FFF; font-size:12px;} </style> <div id="footer"><a href="http://www.sciencenet.cn/aboutus/" target="_blank">关于我们</a> | <a href="http://www.sciencenet.cn/shengming.aspx" target="_blank">网站声明</a> | <a href="http://www.sciencenet.cn/tiaokuang.aspx" target="_blank">服务条款</a> | <a href="http://www.sciencenet.cn/contact.aspx" target="_blank">联系方式</a> | <a href="http://www.sciencenet.cn/RSS.aspx">RSS</a> | <a href="mailto:jubao@stimes.cn">举报</a> | <a href="https://stimes.sciencenet.cn/">中国科学报社</a> <br> <a href="https://beian.miit.gov.cn" target="_blank" > 京ICP备07017567号-12 </a> 互联网新闻信息服务许可证10120230008 京公网安备 11010802032783<br>Copyright @ 2007-<script type="text/javascript">var Date22 = new Date();var year22 = Date22.getFullYear();document.write(year22);</script> 中国科学报社 All Rights Reserved<br> 地址：北京市海淀区中关村南一条乙三号 邮箱：blog@stimes.cn <br> <span style="display:none"> <script type="text/javascript"> var _bdhmProtocol = (("https:" == document.location.protocol) ? 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