This paper is in the following e-collection/theme issue:

Artificial Intelligence (2162) Clinical Information and Decision Making (1935) Digital Health Reviews (1742) Digital Biomarkers and Digital Phenotyping (291) Machine Learning (2095) Decision Support for Health Professionals (1555) Computer-Aided Diagnosis (255) mHealth for Diagnosis (82) Quality/Credibility of eHealth Information and Trust Issues (657) Cancer Prognosis Models and Machine Learning (69) Pathology and Laboratory Medicine (31)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/62851, first published .
Artificial Intelligence in Lymphoma Histopathology: Systematic Review

Artificial Intelligence in Lymphoma Histopathology: Systematic Review

Artificial Intelligence in Lymphoma Histopathology: Systematic Review

Authors of this article:

Yao Fu1 Author Orcid Image ;   Zongyao Huang2 Author Orcid Image ;   Xudong Deng3 Author Orcid Image ;   Linna Xu2 Author Orcid Image ;   Yang Liu2 Author Orcid Image ;   Mingxing Zhang2 Author Orcid Image ;   Jinyi Liu4 Author Orcid Image ;   Bin Huang2 Author Orcid Image
Article Authors Cited by (7) Tweetations Metrics

Journals

  1. Carreras J, Ikoma H, Kikuti Y, Nagase S, Ito A, Orita M, Tomita S, Tanigaki Y, Nakamura N, Masugi Y. Histological Image Classification Between Follicular Lymphoma and Reactive Lymphoid Tissue Using Deep Learning and Explainable Artificial Intelligence (XAI). Cancers 2025;17(15):2428 View
  2. Ogundokun R, Owolawi P, Tu C, van Wyk E. Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach. Tomography 2025;11(8):91 View
  3. Torres Luna J, Tomalieh B, Rebellow D, Kasagga A, Kabir A, Murugesan K, Chavez Cavalie P. AI in Digital Pathology for Diffuse Large B-cell Lymphoma: A Systematic Review of Diagnosis and Classification. Cureus 2025 View
  4. He Z, Jia D, Li Z, Jiang H, Zeng F. IHC-DualNet: a dual-branch graph-based architecture for interpretable and precise immunohistochemistry tissue segmentation. Physics in Medicine & Biology 2025;70(21):215017 View
  5. Albalawi E, Qayyum J, Qayyum J. Artificial intelligence based quantification of T lymphocyte infiltrate predicts prognosis in high grade breast cancer using deep learning and statistical validation. Discover Oncology 2025;16(1) View
  6. Sakaguchi M, Yoshizawa A, Masui K, Sakai T, Komori T. AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue. Cancers 2025;18(1):9 View

Books/Policy Documents

  1. Ochoa-Montiel R, Sánchez-López C, Hernández-Romano M. Advances in Soft Computing. MICAI 2025 Posters Track. View