TY - GEN
T1 - Multi-task learning of a deep K-nearest neighbour network for histopathological image classification and retrieval
AU - Peng, Tingying
AU - Boxberg, Melanie
AU - Weichert, Wilko
AU - Navab, Nassir
AU - Marr, Carsten
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Deep neural networks have achieved tremendous success in image recognition, classification and object detection. However, deep learning is often criticised for its lack of transparency and general inability to rationalise its predictions. The issue of poor model interpretability becomes critical in medical applications: a model that is not understood and trusted by physicians is unlikely to be used in daily clinical practice. In this work, we develop a novel multi-task deep learning framework for simultaneous histopathology image classification and retrieval, leveraging on the classic concept of k-nearest neighbours to improve model interpretability. For a test image, we retrieve the most similar images from our training databases. These retrieved nearest neighbours can be used to classify the test image with a confidence score, and provide a human-interpretable explanation of our classification. Our original framework can be built on top of any existing classification network (and therefore benefit from pretrained models), by (i) combining a triplet loss function with a novel triplet sampling strategy to compare distances between samples and (ii) adding a Cauchy hashing loss function to accelerate neighbour searching. We evaluate our method on colorectal cancer histology slides and show that the confidence estimates are strongly correlated with model performance. Nearest neighbours are intuitive and useful for expert evaluation. They give insights into understanding possible model failures, and can support clinical decision making by comparing archived images and patient records with the actual case.
AB - Deep neural networks have achieved tremendous success in image recognition, classification and object detection. However, deep learning is often criticised for its lack of transparency and general inability to rationalise its predictions. The issue of poor model interpretability becomes critical in medical applications: a model that is not understood and trusted by physicians is unlikely to be used in daily clinical practice. In this work, we develop a novel multi-task deep learning framework for simultaneous histopathology image classification and retrieval, leveraging on the classic concept of k-nearest neighbours to improve model interpretability. For a test image, we retrieve the most similar images from our training databases. These retrieved nearest neighbours can be used to classify the test image with a confidence score, and provide a human-interpretable explanation of our classification. Our original framework can be built on top of any existing classification network (and therefore benefit from pretrained models), by (i) combining a triplet loss function with a novel triplet sampling strategy to compare distances between samples and (ii) adding a Cauchy hashing loss function to accelerate neighbour searching. We evaluate our method on colorectal cancer histology slides and show that the confidence estimates are strongly correlated with model performance. Nearest neighbours are intuitive and useful for expert evaluation. They give insights into understanding possible model failures, and can support clinical decision making by comparing archived images and patient records with the actual case.
UR - http://www.scopus.com/inward/record.url?scp=85075640100&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32239-7_75
DO - 10.1007/978-3-030-32239-7_75
M3 - Conference contribution
AN - SCOPUS:85075640100
SN - 9783030322380
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 676
EP - 684
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -