TY - GEN
T1 - Magnification generalization for histopathology image embedding
AU - Sikaroudi, Milad
AU - Ghojogh, Benyamin
AU - Karray, Fakhri
AU - Crowley, Mark
AU - Tizhoosh, H. R.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/13
Y1 - 2021/4/13
N2 - Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level. Two main approaches for tackling this goal are domain adaptation and domain generalization, where the target magnification levels may or may not be introduced to the model in training, respectively. Although magnification adaptation is a well-studied topic in the literature, this paper, to the best of our knowledge, is the first work on magnification generalization for histopathology image embedding. We use an episodic trainable domain generalization technique for magnification generalization, namely Model Agnostic Learning of Semantic Features (MASF), which works based on the Model Agnostic Meta-Learning (MAML) concept. Our experimental results on a breast cancer histopathology dataset with four different magnification levels show the proposed method's effectiveness for magnification generalization.
AB - Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level. Two main approaches for tackling this goal are domain adaptation and domain generalization, where the target magnification levels may or may not be introduced to the model in training, respectively. Although magnification adaptation is a well-studied topic in the literature, this paper, to the best of our knowledge, is the first work on magnification generalization for histopathology image embedding. We use an episodic trainable domain generalization technique for magnification generalization, namely Model Agnostic Learning of Semantic Features (MASF), which works based on the Model Agnostic Meta-Learning (MAML) concept. Our experimental results on a breast cancer histopathology dataset with four different magnification levels show the proposed method's effectiveness for magnification generalization.
KW - Domain generalization
KW - Histopathology
KW - Magnification
KW - Model agnostic
KW - Semantic features
UR - http://www.scopus.com/inward/record.url?scp=85107189010&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107189010&partnerID=8YFLogxK
U2 - 10.1109/ISBI48211.2021.9433978
DO - 10.1109/ISBI48211.2021.9433978
M3 - Conference contribution
AN - SCOPUS:85107189010
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1864
EP - 1868
BT - 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PB - IEEE Computer Society
T2 - 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Y2 - 13 April 2021 through 16 April 2021
ER -