FibroTrack: a standalone deep learning platform for automated fibrosis quantification in muscle and cardiac histology.

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Bibliographic Details
Title: FibroTrack: a standalone deep learning platform for automated fibrosis quantification in muscle and cardiac histology.
Authors: Odeh A; Department of Genetics and Developmental Biology, The Rappaport Faculty of Medicine and Research Institute, Technion Israel Institute of Technology, Haifa, Israel. Inass.odeh@campus.technion.ac.il., Salem R; Private Clinic, Dentistry and Facial Aesthetics, Deir Hanna, Israel., Saleh MA; Department of Genetics and Developmental Biology, The Rappaport Faculty of Medicine and Research Institute, Technion Israel Institute of Technology, Haifa, Israel., Shemesh A; Biomedical Core Facilities, The Rappaport Faculty of Medicine and Research Institute, Technion Israel Institute of Technology, Haifa, Israel., Stein P; Institute of Pathology and Cytology, Rambam Health Care Campus, Haifa, Israel., Livneh I; Institute of Pathology and Cytology, Rambam Health Care Campus, Haifa, Israel.; The Rappaport Technion Integrated Cancer Center (R-TICC), The Rappaport Faculty of Medicine and Research Institute, Technion Israel Institute of Technology, Haifa, Israel., Hasson P; Department of Genetics and Developmental Biology, The Rappaport Faculty of Medicine and Research Institute, Technion Israel Institute of Technology, Haifa, Israel. phasson@technion.ac.il.
Source: Skeletal muscle [Skelet Muscle] 2026 Jan 26; Vol. 16 (1). Date of Electronic Publication: 2026 Jan 26.
Publication Type: Journal Article
Journal Info: Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 101561193 Publication Model: Electronic Cited Medium: Internet ISSN: 2044-5040 (Electronic) Linking ISSN: 20445040 NLM ISO Abbreviation: Skelet Muscle Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2044-5040
DOI:10.1186/s13395-026-00415-8