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Grant support

The work of N. P. Garcia de la Puente was supported by the grant PID2022-140189OB-C21 funded by MICIU/AEI/10.13039/501100011033 ERDF/UE and FSE+. The work of M. Lopez-Perez was supported by the grant JDC2022-048318-I funded by MICIU/AEI/10.13039/501100011033 and the "European Union NextGenerationEU/PRTR". This work was also supported by the project PID2022-140189OB-C21 (ASSIST) funded by MICIU/AEI/10.13039/501100011033 and by "FEDER, EU".

Analysis of institutional authors

Garcia-De-La-Puente, Natalia PAuthorLópez-Pérez, MiguelCorresponding AuthorLaunet, LaëtitiaAuthorNaranjo, ValeryAuthor

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Proceedings Paper

Domain Adaptation for Unsupervised Cancer Detection: An Application for Skin Whole Slides Images from an Interhospital Dataset

Publicated to:Lecture Notes In Computer Science. 15004 58-68 - 2024-01-01 15004(), DOI: 10.1007/978-3-031-72083-3_6

Authors: Garcia-de-la-Puente, Natalia P; Lopez-Perez, Miguel; Launet, Laetitia; Naranjo, Valery

Affiliations

Univ Politecn Valencia, Inst Univ Invest Tecnol Ctr Ser Humano, Valencia, Spain - Author

Abstract

Skin cancer diagnosis relies on assessing the histopathological appearance of skin cells and the patterns of epithelial skin tissue architecture. Despite recent advancements in deep learning for automating skin cancer detection, two main challenges persist for their clinical deployment. (1) Deep learning models only recognize the classes trained on, giving arbitrary predictions for rare or unknown diseases. (2) The generalization across healthcare institutions, as variations arising from diverse scanners and staining procedures, increase the task complexity. We propose a novel Domain Adaptation method for Unsupervised cancer Detection (DAUD) using whole slide images to address these concerns. Our method consists of an autoencoder-based model with stochastic latent variables that reflect each institution's features. We have validated DAUD in a real-world dataset from two different hospitals. In addition, we utilized an external dataset to evaluate the capability for out-of-distribution detection. DAUD demonstrates comparable or superior performance to the state-of-the-art methods for anomaly detection https://github.com/cvblab/DAUD-MICCAI2024.

Keywords

HistopathologSkin cancerUnsupervised detection

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Lecture Notes In Computer Science due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 13/61, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Theory & Methods. Notably, the journal is positioned above the 90th percentile.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Pérez García de la Puente, Natalia Lourdes) and Last Author (Naranjo Ornedo, Valeriana).

the author responsible for correspondence tasks has been López Pérez, Miguel.