ArticlePatterns (New York, N.Y.)2021
Knowledge transfer to enhance the performance of deep learning models for automated classification of B cell neoplasms.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Self-explaining artificial intelligence for the classification of B cell non-Hodgkin lymphoma: A diagnostic decision support study.PLoS medicine · 2026Article
- Decoding Immunodeficiencies with Artificial Intelligence: A New Era of Precision Medicine.Biomedicines · 2025Review
- Machine Learning Methods in Clinical Flow Cytometry.Cancers · 2025Review
- Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.Cytometry. Part B, Clinical cytometry · 2024Article
- Declining incidence and improving survival of ocular and orbital lymphomas in the US between 1995 and 2018.Scientific reports · 2024Article
- Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders.Diagnostics (Basel, Switzerland) · 2024Article
- Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics.Antibiotics (Basel, Switzerland) · 2022Article
- MLP-PSO Hybrid Algorithm for Heart Disease Prediction.Journal of personalized medicine · 2022Article
- Application of machine learning in the management of lymphoma: Current practice and future prospects.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multi-parameter flow cytometry (MFC) is a cornerstone in clinical decision making for leukemia and lymphoma. MFC data analysis requires manual gating of cell populations, which is time-consuming, subjective, and often limited to a two-dimensional space. In recent years, deep learning models have been successfully used to analyze data in high-dimensional space and are highly accurate. However, AI models used for disease classification with MFC data are limited to the panel they were trained on. Thus, a key challenge in deploying AI into routine diagnostics is the robustness and adaptability of such models. This study demonstrates how transfer learning can be applied to boost the performance of models with smaller datasets acquired with different MFC panels. We trained models for four additional datasets by transferring the features learned from our base model. Our workflow increased the model's overall performance and, more prominently, improved the learning rate for small training sizes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.