TY - GEN
T1 - Unsupervised Representation Learning from Pathology Images with Multi-directional Contrastive Predictive Coding
AU - Carse, Jacob
AU - Carey, Frank
AU - McKenna, Stephen
N1 - Funding Information:
This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC Training Grant EP/N509632/1). The authors are grateful to the Computer Vision and Image Processing research group for helpful motivating discussions.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/25
Y1 - 2021/5/25
N2 - Digital pathology tasks have benefited greatly from modern deep learning algorithms. However, their need for large quantities of annotated data has been identified as a key challenge. This need for data can be countered by using unsupervised learning in situations where data are abundant but access to annotations is limited. Feature representations learned from unannotated data using contrastive predictive coding (CPC) have been shown to enable classifiers to obtain state of the art performance from relatively small amounts of annotated computer vision data. We present a modification to the CPC framework for use with digital pathology patches. This is achieved by introducing an alternative mask for building the latent context and using a multi-directional PixelCNN autoregressor. To demonstrate our proposed method we learn feature representations from the Patch Camelyon histology dataset. We show that our proposed modification can yield improved deep classification of histology patches.
AB - Digital pathology tasks have benefited greatly from modern deep learning algorithms. However, their need for large quantities of annotated data has been identified as a key challenge. This need for data can be countered by using unsupervised learning in situations where data are abundant but access to annotations is limited. Feature representations learned from unannotated data using contrastive predictive coding (CPC) have been shown to enable classifiers to obtain state of the art performance from relatively small amounts of annotated computer vision data. We present a modification to the CPC framework for use with digital pathology patches. This is achieved by introducing an alternative mask for building the latent context and using a multi-directional PixelCNN autoregressor. To demonstrate our proposed method we learn feature representations from the Patch Camelyon histology dataset. We show that our proposed modification can yield improved deep classification of histology patches.
KW - Digital Pathology
KW - Medical Imaging
KW - Representation Learning
KW - Semi-Supervised Learning
UR - https://arxiv.org/abs/2105.05345
UR - https://virtual.biomedicalimaging.org/isbi/2021/isbi-2021/315261/jacob.carse.unsupervised.representation.learning.from.pathology.images.with.html?f=listing%3D1%2Abrowseby%3D8%2Asortby%3D2%2Amedia%3D2%2Ace_id%3D1979%2Aot_id%3D24333
UR - http://www.scopus.com/inward/record.url?scp=85107183330&partnerID=8YFLogxK
U2 - 10.1109/ISBI48211.2021.9434140
DO - 10.1109/ISBI48211.2021.9434140
M3 - Conference contribution
SN - 9781665429474
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1254
EP - 1258
BT - 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PB - IEEE
T2 - 2021 IEEE International Symposium on Biomedical Imaging (ISBI)
Y2 - 13 April 2021 through 16 April 2021
ER -
