TY - GEN
T1 - Learning Similarity via Subjective Evaluations and Deep Features of Histopathology Images
AU - Hosseini, S. Maryam
AU - Babaie, Morteza
AU - Tizhoosh, H. R.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Visual similarity estimation for histopathology images plays a key role in many medical imaging tasks, especially in image search and retrieval. All image similarity evaluation approaches employ distance-based metrics to quantify the degree of (dis) similarity. However, it has always been challenging to numerically estimate the similarity between two images, which is compatible with subjective assessment of the human operator, i.e., physicians such as radiologists and pathologists. Relying only on distance calculations through Euclidean, Manhattan, Hamming, and cosine distances does not provide us with the result that can be translated to human judgment in linguistic terms and/or in a normalized range. There is a need for a reliable image similarity measurement compatible with the human assessment with minimum possible conflict. This work proposes a new scheme that evaluates the similarity between a pair of histopathology images close to human reasoning using a fuzzy-logic approach. To this end, we developed a web application to interface with users and to collect descriptive image similarity data for training and testing purposes. We designed an adaptive neuro-fuzzy inference system (ANFIS) to model the vague and uncertain nature of user image assessment for the histopathology image comparison task. The experimental results show that the trained ANFIS can estimate the image similarity with acceptable accuracy and consistent with the user evaluations.
AB - Visual similarity estimation for histopathology images plays a key role in many medical imaging tasks, especially in image search and retrieval. All image similarity evaluation approaches employ distance-based metrics to quantify the degree of (dis) similarity. However, it has always been challenging to numerically estimate the similarity between two images, which is compatible with subjective assessment of the human operator, i.e., physicians such as radiologists and pathologists. Relying only on distance calculations through Euclidean, Manhattan, Hamming, and cosine distances does not provide us with the result that can be translated to human judgment in linguistic terms and/or in a normalized range. There is a need for a reliable image similarity measurement compatible with the human assessment with minimum possible conflict. This work proposes a new scheme that evaluates the similarity between a pair of histopathology images close to human reasoning using a fuzzy-logic approach. To this end, we developed a web application to interface with users and to collect descriptive image similarity data for training and testing purposes. We designed an adaptive neuro-fuzzy inference system (ANFIS) to model the vague and uncertain nature of user image assessment for the histopathology image comparison task. The experimental results show that the trained ANFIS can estimate the image similarity with acceptable accuracy and consistent with the user evaluations.
KW - ANFIS
KW - Deep Features
KW - Histopathology
KW - Image Similarity
KW - Similarity Metrics
KW - Subjective Similarity
UR - http://www.scopus.com/inward/record.url?scp=85123684806&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123684806&partnerID=8YFLogxK
U2 - 10.1109/BIBE52308.2021.9635177
DO - 10.1109/BIBE52308.2021.9635177
M3 - Conference contribution
AN - SCOPUS:85123684806
T3 - BIBE 2021 - 21st IEEE International Conference on BioInformatics and BioEngineering, Proceedings
BT - BIBE 2021 - 21st IEEE International Conference on BioInformatics and BioEngineering, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on BioInformatics and BioEngineering, BIBE 2021
Y2 - 25 October 2021 through 27 October 2021
ER -