{"id":189,"date":"2025-04-02T23:03:47","date_gmt":"2025-04-02T15:03:47","guid":{"rendered":"http:\/\/106.52.84.14\/?p=189"},"modified":"2025-04-02T23:03:47","modified_gmt":"2025-04-02T15:03:47","slug":"19-13-mics%e5%9c%a8%e7%ba%bf%e5%ad%a6%e6%9c%af%e8%ae%b2%e5%ba%a7%ef%bc%9axiaolei-huang","status":"publish","type":"post","link":"https:\/\/www.mics-ai.com\/index.php\/2025\/04\/02\/19-13-mics%e5%9c%a8%e7%ba%bf%e5%ad%a6%e6%9c%af%e8%ae%b2%e5%ba%a7%ef%bc%9axiaolei-huang\/","title":{"rendered":"[#19-13] MICS\u5728\u7ebf\u5b66\u672f\u8bb2\u5ea7\uff1aXiaolei Huang"},"content":{"rendered":"\n
\u5609\u5bbe<\/strong><\/td>Prof. Xiaolei Huang\uff08Pennsylvania State University\uff09<\/td><\/tr>
\u65f6\u95f4<\/strong><\/td>2019\u5e747\u670830\u65e5\uff08\u661f\u671f\u4e8c\uff09\u4e0b\u534820:00\uff08\u5317\u4eac\u65f6\u95f4\uff09<\/td><\/tr>
\u9898\u76ee<\/strong><\/td>Learning Biomarkers from Biomedical Image Data<\/td><\/tr>
\u4e3b\u6301<\/strong><\/td>\u4f55\u6656\u5149\uff08\u4e2d\u56fd\u79d1\u5b66\u9662\u81ea\u52a8\u5316\u7814\u7a76\u6240\uff09<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n
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\u62a5\u544a\u6458\u8981<\/strong>
From automated cancer detection to smart home devices, artificial intelligence research especially deep learning has revolutionized a wide variety of domains from image classification and speech recognition to genomics and drug discovery. In this talk, I will introduce my research that centers around machine learning and image analysis techniques for automatically discovering biomarkers from complex biomedical images.   I will present Cervitor, an AI system that learns useful features from large amounts of data including images and other clinical tests to make a diagnosis about cervical cancer.  I will show SOAX and TROAX, which are open source software that automatically extract and track the growth and deformation of biopolymer networks from 2D and 3D time-lapse sequences imaged by various microscopic imaging modalities.  I will also demonstrate several novel conditional Generative Adversarial Network based approaches for medical training data augmentation, image segmentation, and high-resolution image synthesis. The talk will conclude with a quick review of other recent work including computer-aided diagnosis of label-free 3D OCM images of breast tissue, and recurrent neural nets with attention for clinical report generation. <\/p>\n\n\n\n

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  1. T. Xu, H. Zhang, C. Xin, E. Kim, L.R. Long, Z. Xue, S. Antani, X. Huang, “Multi-feature based benchmark for cervical dysplasia classification evaluation,” In Pattern Recognition, Vol. 63, pp. 468-475, 2017.<\/li>\n\n\n\n
  2. T. Xu, H. Zhang, X. Huang, S. Zhang, D. Metaxas, “Multimodal Deep Learning for Cervical Dysplasia Diagnosis,” In Proc. of International Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI), LNCS Vol. 9901, pp. 115-123, 2016.<\/li>\n\n\n\n
  3. T. Xu, C. Langouras, M.A. Koudehi, B.E. Vos, N. Wang, G.H. Koenderink, X. Huang, D. Vavylonis, “Automated Tracking of Biopolymer Growth and Network Deformation with TSOAX,” In Scientific Reports, 9(1), p. 1717, 2019.<\/li>\n\n\n\n
  4. H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, D. Metaxas, “StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks,” In IEEE Trans. On Pattern Analysis and Machine Intelligence, 41(8):1947-1962, 2018.<\/li>\n\n\n\n
  5. T. Xu, P. Zhang, Q. Huang, H. Zhang, Z. Gan, X. Huang, X. He, “AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks,” In Proc. Of IEEE Conf. on Computer Vision and Pattern Recognition, 2018.<\/li>\n\n\n\n
  6. Y. Xue, T. Xu, H. Zhang, L. R. Long, and X. Huang, “SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation,” In Neuroinformatics, 16(3-4):383-392, 2018.<\/li>\n\n\n\n
  7. Y. Xue, T. Xu, L.R. Long, Z. Xue, S. Antani, G.R. Thoma, X. Huang, “Multimodal Recurrent Model with Attention for Automated Radiology Report Generation,” In Proc. Of International Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 457-466, 2018.<\/li>\n\n\n\n
  8. Y. Xue, X. Huang, “Improved Disease Classification in Chest X-rays with Transferred Features from Report Generation,” In Proc. of International Conf. on Information Processing in Medical Imaging (IPMI), pp. 125-138, 2019.<\/li>\n<\/ol>\n\n\n\n

    \u5609\u5bbe\u7b80\u4ecb<\/strong><\/p>\n\n\n\n

    Dr. Sharon Xiaolei Huang is currently an associate professor in the College of Information Sciences and Technology at the Pennsylvania State University, University Park, PA, USA.  She is also an affiliated faculty member of Penn State\u2019s Huck Institutes of the Life Sciences. Her research interests lie at the interface between biomedical image analysis, machine learning, and computer vision.  She has over 130 publications (including journal articles, book chapters, and refereed conference papers) and holds 7 patents. She is an associate editor for the Computer Vision and Image Understanding journal. She received her Bachelor\u2019s degree in computer science from Tsinghua University, and her Master\u2019s and doctoral degrees in computer science from Rutgers University. Her research has been funded by the NIH, NSF, the Howard Hughes Medical Institute, and the Pennsylvania state.<\/p>\n\n\n\n

    \u7279\u522b\u9e23\u8c22\u672c\u6b21Webinar\u4e3b\u8981\u7ec4\u7ec7\u8005\uff1a<\/strong>
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    1. MICS\u5728\u7ebf\u5b66\u672f\u8bb2\u5ea7\u4f9d\u6258\u5728\u7ebf\u76f4\u64ad\u5e73\u53f0\u8fdb\u884c\uff0c\u542c\u4f17\u8bf7\u70b9\u51fb\u76f4\u64ad\u94fe\u63a5https:\/\/live.polyv.cn\/watch\/352994\uff08\u6ce8\uff1a\u8be5\u94fe\u63a5\u4e3a07\u670830\u65e5\u62a5\u544a\u94fe\u63a5\uff0c\u8be5\u94fe\u63a5\u6bcf\u671f\u4f1a\u548c\u8bb2\u8005\u4fe1\u606f\u4e00\u8d77\u66f4\u65b0\uff09\u53c2\u52a0\u6d3b\u52a8\uff0c\u652f\u6301\u5b89\u88c5Windows\u7cfb\u7edf\u7684\u7535\u8111\u3001MAC\u7535\u8111\u3001\u624b\u673a\u7b49\u8bbe\u5907\uff1b\u624b\u673a\u5ba2\u6237\u7aef\u4e5f\u53ef\u76f4\u63a5\u626b\u63cf\u4e8c\u7ef4\u7801\u8fdb\u5165\u76f4\u64ad\uff1b<\/p>\n\n\n

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    2. \u6d3b\u52a8\u5f00\u59cb\u524d5\u5206\u949f\u5de6\u53f3\uff0c\u8bb2\u8005\u4f1a\u5f00\u542f\u76f4\u64ad\uff0c\u6d3b\u52a8\u65f6\u8bb2\u8005\u4f1a\u4e0a\u4f20PPT\u6216\u5171\u4eab\u5c4f\u5e55\uff0c\u542c\u4f17\u53ef\u4ee5\u770b\u5230Slides\uff0c\u542c\u5230\u8bb2\u8005\u7684\u8bed\u97f3\uff0c\u5e76\u901a\u8fc7\u804a\u5929\u529f\u80fd\u4e0e\u8bb2\u8005\u4ea4\u4e92\uff1b<\/p>\n\n\n\n

    3. \u6d3b\u52a8\u8fc7\u7a0b\u4e2d\uff0c\u8bf7\u4e0d\u8981\u8bf4\u65e0\u5173\u8bdd\u8bed\uff0c\u4ee5\u514d\u5f71\u54cd\u6d3b\u52a8\u6b63\u5e38\u8fdb\u884c\uff1b\u6d3b\u52a8\u8fc7\u7a0b\u4e2d\uff0c\u5982\u51fa\u73b0\u542c\u4e0d\u5230\u6216\u770b\u4e0d\u5230\u89c6\u9891\u7b49\u95ee\u9898\uff0c\u5efa\u8bae\u9000\u51fa\u518d\u91cd\u65b0\u8fdb\u5165\uff0c\u4e00\u822c\u90fd\u80fd\u89e3\u51b3\u95ee\u9898\uff1b\u5efa\u8bae\u901a\u8fc7\u901f\u5ea6\u8f83\u5feb\u7684\u7f51\u7edc\u53c2\u52a0\u6d3b\u52a8\uff0c\u4f18\u5148\u91c7\u7528\u6709\u7ebf\u7f51\u7edc\u8fde\u63a5\uff1b<\/p>\n\n\n\n

    4. \u540e\u7eed\u6bcf\u671f\u8bb2\u5ea7\u4fe1\u606f\uff0c\u4f1a\u901a\u8fc7MICS\u5fae\u4fe1\u516c\u4f17\u53f7\u201c\u533b\u5b66\u56fe\u50cf\u8ba1\u7b97\u9752\u5e74\u7814\u8ba8\u4f1a\u201d\uff08\u626b\u63cf\u4e0b\u65b9\u4e8c\u7ef4\u7801\u5173\u6ce8MICS\u5fae\u4fe1\u516c\u4f17\u53f7\uff09\u6216MICS QQ\u7fa4\uff08\u7fa4\u53f7\uff1a641894878\uff09\u8fdb\u884c\u901a\u77e5\uff08\u6ce8\uff1a\u7533\u8bf7\u52a0\u5165MICS QQ\u7fa4\u65f6\u9700\u9a8c\u8bc1\u59d3\u540d\u3001\u5355\u4f4d\u548c\u8eab\u4efd\uff0c\u7f3a\u4e00\u4e0d\u53ef\uff1b\u5165\u7fa4\u540e\uff0c\u8bf7\u5b9e\u540d\uff0c\u59d3\u540d\u8eab\u4efd\u5355\u4f4d\uff1b\u8eab\u4efd\uff1a\u5b66\u6821\u53ca\u79d1\u7814\u5355\u4f4d\u4eba\u5458T\uff0c\u4f01\u4e1a\u7814\u53d1I\uff0c\u535a\u58ebD\uff0c\u7855\u58ebM\uff09\u3002<\/p>\n\n\n

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    MICS\u5728\u7ebf\u5b66\u672f\u8bb2\u5ea7\u7684\u6a21\u5f0f\u548c\u7ec4\u7ec7\u65b9\u5f0f\u501f\u9274\u4e86\u5f88\u591aVALSE\u7684\u7ecf\u9a8c\uff0c\u4eceVALSE\u5f97\u5230\u4e86\u5f88\u591a\u7684\u542f\u53d1\uff0c\u5728\u6b64\u5bf9VALSE\u7ec4\u59d4\u4f1a\u8868\u793a\u8877\u5fc3\u7684\u611f\u8c22\uff0c\u4e5f\u795d\u613fMICS\u548cVALSE\u8d8a\u529e\u8d8a\u597d\uff01<\/p>\n\n\n\n

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    [#18-01] \u6c88\u5b9a\u521a-\u5317\u5361\u7f57\u6765\u7eb3\u5927\u5b66\u6559\u5802\u5c71\u5206\u6821<\/a><\/p>\n\n\n\n

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    [#19-10] \u859b\u5fe0-\u4e0a\u6d77\u8054\u5f71\u667a\u80fd\u533b\u7597\u79d1\u6280\u6709\u9650\u516c\u53f8<\/a><\/p>\n\n\n\n

    [#19-11] \u5f20\u5bd2-\u5317\u5361\u7f57\u6765\u7eb3\u5927\u5b66\u6559\u5802\u5c71\u5206\u6821<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

    \u5609\u5bbe Prof. Xiaolei Hua…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-189","post","type-post","status-publish","format-standard","hentry","category-4"],"_links":{"self":[{"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/posts\/189","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/comments?post=189"}],"version-history":[{"count":0,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/posts\/189\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/media?parent=189"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/categories?post=189"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/tags?post=189"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}