کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11030067 | 1646392 | 2019 | 38 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images. Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location and appearance of nuclei. A primary contribution of our work is the development of an unsupervised detection network by using the characteristics of histopathology image patches. The pretrained nucleus detection and feature extraction modules in our CAE can be fine-tuned for supervised learning in an end-to-end fashion. We evaluate our method on four datasets and achieve state-of-the-art results. In addition, we are able to achieve comparable performance with only 5% of the fully-supervised annotation cost.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 86, February 2019, Pages 188-200
Journal: Pattern Recognition - Volume 86, February 2019, Pages 188-200
نویسندگان
Le Hou, Vu Nguyen, Ariel B. Kanevsky, Dimitris Samaras, Tahsin M. Kurc, Tianhao Zhao, Rajarsi R. Gupta, Yi Gao, Wenjin Chen, David Foran, Joel H. Saltz,