报告题目：医学图像分析以及可解释性
时间：8月16日上午10:00-11:00
地点：计算所446会议室
报告摘要：
Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch’s Postulates, a well-known strategy in medical research to identify the property of pathogen, we define a pathological descriptor that can be extracted from the activated neurons of a diabetic retinopathy detector. To visualize the symptom and feature encoded in this descriptor, we propose a GAN based method to synthesize pathological retinal image given the descriptor and a binary vessel segmentation. Besides, with this descriptor, we can arbitrarily manipulate the position and quantity of lesions. As verified by a panel of 5 licensed ophthalmologists, our synthesized images carry the symptoms that are directly related to diabetic retinopathy diagnosis. The panel survey also shows that our generated images is both qualitatively and quantitatively superior to existing methods.
报告人简介：
2014年获澳大利亚澳大利亚国立大学博士学位，2014年至2016年在新加坡 A*STAR担任博士后研究员。2016年加入日本国立信息学研究所担任特任研究员并兼任京都大学客任研究员。同时谷林博士参与日本内阁府下光超声医疗设备研发项目，并具体负责图像处理算法以及临床实际验证工作。谷林博士研究方向包括通用人工智能技术以及其在医学图像以及计算摄影学等方面应用。目前在本领域高水平国际期刊和国际会议上录用发表论文十余篇。其中多篇论文发表在顶级期刊 TIP, TMI及顶级会议 ICCV, CVPR，MICCAI 上，工作得到了国内外同行的关注和认可。曾分别获国际模式识别学会以及澳大利亚模式识别学会最佳学生论文奖等。
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