Benchmarking Deep Learning-Based Image Retrieval of Oral Tumor Histology.

Introduction:  Oral tumors necessitate a dependable computer-assisted pathological diagnosis system considering their rarity and diversity. A content-based image retrieval (CBIR) system using deep neural networks has been successfully devised for digital pathology. No CBIR system for oral pathology has been investigated because of the lack of an extensive image database and feature extractors tailored to oral pathology.
Materials and Methods: This study uses a large CBIR database constructed from 30 categories of oral tumors to compare deep learning methods as feature extractors.
Results: The highest average area under the receiver operating characteristic curve (AUC) was achieved by models trained on database images using self-supervised learning (SSL) methods (0.900 with SimCLR and 0.897 with TiCo). The generalizability of the models was validated using query images from the same cases taken with smartphones. When smartphone images were tested as queries, both models yielded the highest mean AUC (0.871 with SimCLR and 0.857 with TiCo). We ensured the retrieved image result would be easily observed by evaluating the top 10 mean accuracies and checking for an exact diagnostic category and its differential diagnostic categories.
Conclusion: Training deep learning models with SSL methods using image data specific to the target site is beneficial for CBIR tasks in oral tumor histology to obtain histologically meaningful results and high performance. This result provides insight into the effective development of a CBIR system to help improve the accuracy and speed of histopathology diagnosis and advance oral tumor research in the future.
Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Institutional Review Board of Tokyo Medical and Dental University issued approval D2019-087. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: This study was supported by AMED Practical Research for Innovative Cancer Control under grant number JP 23ck0106640 to S.I. and the JSPS KAKENHI Grant-in-Aid for Scientific Research (B) under grant number 21H03836 to D.K. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.
(Copyright © 2024, Herdiantoputri et al.)