Supervised Intra-embedding of Fisher Vectors for Histopathology Image Classification Supervised Intra-embedding of Fisher Vectors for Histopathology Image Classification
Paper summary The goal of this work is to classify histopathology images into benign and malignant. They use the BreaKHis and IICBU 2008 lymphoma datasets. They use a VGG network for feature extraction from each image. Then on these VGG feature vectors they learn [Fisher Vectors ](https://prateekvjoshi.com/2014/08/23/image-classification-using-fisher-vectors/) which they use to make a prediction. It is unclear why Fisher Vectors are more useful than the fully connected layers of the VGG net that they replace. It is not clear how much analysis was performed for the VGG baseline. Also, as a baseline a VGG network should have been trained from scratch to extract domain specific features. Poster: https://i.imgur.com/fgzmeYv.png
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Supervised Intra-embedding of Fisher Vectors for Histopathology Image Classification
Song, Yang and Chang, Hang and Huang, Heng and Cai, Weidong
Medical Image Computing and Computer Assisted Interventions Conference - 2017 via Bibsonomy
Keywords: dblp


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