When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections
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- 作者：Cheng Zhonga ; Ju Hana ; Alexander Borowskyc ; Bahram Parvinb ; Yunfu Wanga ; d ; ; Hang Chang ; a ; hchang@lbl.gov" class="auth_mail" title="E-mail the corresponding author
- 关键词：Computational histopathology ; Classification ; Unsupervised feature learning ; Sparse feature encoder
- 刊名：Medical Image Analysis
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：35
- 期：Complete
- 页码：530-543
- 全文大小：3291 K
文摘
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ra0001">Both Cellular Morphometric Feature and features from unsupervised feature learning lead to superior performance when compared to SIFT and [Color, Texture];
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ra0002">Cellular saliency incorporation impairs the performance for systems built upon pixel-/patch-level features; and,
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ra0003">The effect of model architecture is correlated with the robustness of features, and the performance can be consistently improved by the deep-extension of systems built upon both Cellular Morphometric Feature and features from unsupervised feature learning.