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This work has received funding from Horizon 2020, the European Union's Framework Programme for Research and Innovation, under the grant agreement No. 860627 (CLARIFY) , the Spanish Ministry of Economy and Competitiveness through project PID2019-105142RB-C21 (AI4SKIN) and GVA through the project INNEST/2021/321 (SAMUEL) . he work of Cristian Camilo Pulgarin Ospina has been supported by the Spanish State Research Agency (PRE2020-093271) . The work of Rocio del Amor has been supported by the Spanish Ministry of Universities (FPU20/05263) . The work of J. Silva-Rodriguez was carried out during his previous position at Universitat Politecnica de Valencia.

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HistoColAi: An open-source web platform for collaborative digital histology image annotation with AI-driven predictive integration

Publicat a:Computer Methods And Programs In Biomedicine. 260 108577- - 2025-03-01 260(), DOI: 10.1016/j.cmpb.2024.108577

Autors: Pulgarin-Ospina, Cristian Camilo; del Amor, Rocio; Silva-Rodriguez, Julio Jose; Colomer, Adrian; Naranjo, Valery

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Digital pathology is now a standard component of the pathology workflow, offering numerous benefits such as high-detail whole slide images and the capability for immediate case sharing between hospitals. Recent advances in deep learning-based methods for image analysis make them a potential aid in digital pathology. However, A significant challenge in developing computer-aided diagnostic systems for pathology is the lack of intuitive, open-source web applications for data annotation. This paper proposes a web service that efficiently provides a tool to visualize and annotate digitized histological images, integrating AI-driven predictive insights. While the tool is capable of handling various image formats, its primary use case is for Whole Slide Imaging (WSI) in the TIFF format, specifically tailored for histopathology applications. This innovative integration not only revolutionizes accessibility but also democratizes the utilization of complex deep-learning models for pathologists unfamiliar with such tools. Moreover, to demonstrate the effectiveness of this approach, we present a use case centered on the diagnosis of spindle cell skin neoplasm involving multiple annotators. Additionally, we conduct a usability study, showing the feasibility of the developed tool.

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