TY - GEN
T1 - ALFA - Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals
AU - Sikaroudi, Milad
AU - Hosseini, Maryam
AU - Rahnamayan, Shahryar
AU - Tizhoosh, H. R.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We propose an exhaustive methodology that leverages all levels of feature abstraction, targeting an enhancement in the generalizability of image classification to unobserved hospitals. Our approach incorporates augmentation-based self-supervision with common distribution shifts in histopathology scenarios serving as the pretext task. This enables us to derive invariant features from training images without relying on training labels, thereby covering different abstraction levels. Moving onto the subsequent abstraction level, we employ a domain alignment module to facilitate further extraction of invariant features across varying training hospitals. To represent the highly specific features of participating hospitals, an encoder is trained to classify hospital labels, independent of their diagnostic labels. The features from each of these encoders are subsequently disentangled to minimize redundancy and segregate the features. This representation, which spans a broad spectrum of semantic information, enables the development of a model demonstrating increased robustness to unseen images from disparate distributions. Experimental results from the PACS dataset (a domain generalization benchmark), a synthetic dataset created by applying histopathology-specific jitters to the MHIST dataset (defining different domains with varied distribution shifts), and a Renal Cell Carcinoma dataset derived from four image repositories from TCGA, collectively indicate that our proposed model is adept at managing varying levels of image granularity. Thus, it shows improved generalizability when faced with new, out-of-distribution hospital images. The code is available at: https://github.com/miladsikaroudi/ALFA.
AB - We propose an exhaustive methodology that leverages all levels of feature abstraction, targeting an enhancement in the generalizability of image classification to unobserved hospitals. Our approach incorporates augmentation-based self-supervision with common distribution shifts in histopathology scenarios serving as the pretext task. This enables us to derive invariant features from training images without relying on training labels, thereby covering different abstraction levels. Moving onto the subsequent abstraction level, we employ a domain alignment module to facilitate further extraction of invariant features across varying training hospitals. To represent the highly specific features of participating hospitals, an encoder is trained to classify hospital labels, independent of their diagnostic labels. The features from each of these encoders are subsequently disentangled to minimize redundancy and segregate the features. This representation, which spans a broad spectrum of semantic information, enables the development of a model demonstrating increased robustness to unseen images from disparate distributions. Experimental results from the PACS dataset (a domain generalization benchmark), a synthetic dataset created by applying histopathology-specific jitters to the MHIST dataset (defining different domains with varied distribution shifts), and a Renal Cell Carcinoma dataset derived from four image repositories from TCGA, collectively indicate that our proposed model is adept at managing varying levels of image granularity. Thus, it shows improved generalizability when faced with new, out-of-distribution hospital images. The code is available at: https://github.com/miladsikaroudi/ALFA.
KW - Domain Generalization
KW - Generalization
KW - Invariant features
KW - Multi hospital
KW - Specific features
UR - http://www.scopus.com/inward/record.url?scp=85182921368&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182921368&partnerID=8YFLogxK
U2 - 10.1109/ICCVW60793.2023.00281
DO - 10.1109/ICCVW60793.2023.00281
M3 - Conference contribution
AN - SCOPUS:85182921368
T3 - Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
SP - 2656
EP - 2665
BT - Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
Y2 - 2 October 2023 through 6 October 2023
ER -