TY - GEN
T1 - Patch Clustering for Representation of Histopathology Images
AU - Chenni, Wafa
AU - Herbi, Habib
AU - Babaie, Morteza
AU - Tizhoosh, Hamid R.
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Whole Slide Imaging (WSI) has become an important topic during the last decade. Even though significant progress in both medical image processing and computational resources has been achieved, there are still problems in WSI that need to be solved. A major challenge is the scan size. The dimensions of digitized tissue samples may exceed 100,000 by 100,000 pixels causing memory and efficiency obstacles for real-time processing. The main contribution of this work is representing a WSI by selecting a small number of patches for algorithmic processing (e.g., indexing and search). As a result, we reduced the search time and storage by various factors between (50%–90%), while losing only a few percentages in the patch retrieval accuracy. A self-organizing map (SOM) has been applied on local binary patterns (LBP) and deep features of the KimiaPath24 dataset in order to cluster patches that share the same characteristics. We used a Gaussian mixture model (GMM) to represent each class with a rather small (10%–50%) portion of patches. The results showed that LBP features can outperform deep features. By selecting only 50% of all patches after SOM clustering and GMM patch selection, we received 65% accuracy for retrieval of the best match, while the maximum accuracy (using all patches) was 69%.
AB - Whole Slide Imaging (WSI) has become an important topic during the last decade. Even though significant progress in both medical image processing and computational resources has been achieved, there are still problems in WSI that need to be solved. A major challenge is the scan size. The dimensions of digitized tissue samples may exceed 100,000 by 100,000 pixels causing memory and efficiency obstacles for real-time processing. The main contribution of this work is representing a WSI by selecting a small number of patches for algorithmic processing (e.g., indexing and search). As a result, we reduced the search time and storage by various factors between (50%–90%), while losing only a few percentages in the patch retrieval accuracy. A self-organizing map (SOM) has been applied on local binary patterns (LBP) and deep features of the KimiaPath24 dataset in order to cluster patches that share the same characteristics. We used a Gaussian mixture model (GMM) to represent each class with a rather small (10%–50%) portion of patches. The results showed that LBP features can outperform deep features. By selecting only 50% of all patches after SOM clustering and GMM patch selection, we received 65% accuracy for retrieval of the best match, while the maximum accuracy (using all patches) was 69%.
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U2 - 10.1007/978-3-030-23937-4_4
DO - 10.1007/978-3-030-23937-4_4
M3 - Conference contribution
AN - SCOPUS:85069233690
SN - 9783030239367
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 28
EP - 37
BT - Digital Pathology - 15th European Congress, ECDP 2019, Proceedings
A2 - Reyes-Aldasoro, Constantino Carlos
A2 - Janowczyk, Andrew
A2 - Veta, Mitko
A2 - Bankhead, Peter
A2 - Sirinukunwattana, Korsuk
PB - Springer Verlag
T2 - 15th European Congress on Digital Pathology, ECDP 2019
Y2 - 10 April 2019 through 13 April 2019
ER -