TY - GEN
T1 - Classification and Retrieval of Digital Pathology Scans
T2 - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
AU - Babaie, Morteza
AU - Kalra, Shivam
AU - Sriram, Aditya
AU - Mitcheltree, Christopher
AU - Zhu, Shujin
AU - Khatami, Amin
AU - Rahnamayan, Shahryar
AU - Tizhoosh, Hamid R.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/22
Y1 - 2017/8/22
N2 - In this paper, we introduce a new dataset, Kimia Path24, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000x1000 (0.5mm x 0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80% for CNN.
AB - In this paper, we introduce a new dataset, Kimia Path24, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000x1000 (0.5mm x 0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80% for CNN.
UR - http://www.scopus.com/inward/record.url?scp=85030229949&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030229949&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2017.106
DO - 10.1109/CVPRW.2017.106
M3 - Conference contribution
AN - SCOPUS:85030229949
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 760
EP - 768
BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
PB - IEEE Computer Society
Y2 - 21 July 2017 through 26 July 2017
ER -