ArticleCell reports methods2023
MIA is an open-source standalone deep learning application for microscopic image analysis.
Article in Cell reports methods, 2023. 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, 18 citations in OpenAlex.
- Circulating Neutrophils Exhibit an Altered Immune Response in Chronic LUTS: An Image-Based Deep Learning Analysis.International urogynecology journal · 2026Article
- Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions.eLife · 2026Article
- Phenotypic Characterization Using Open-Source Deep Learning Tools.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Reanalysis of Published Histological Data Can Help to Characterize Neuronal Death After Spinal Cord Injury.International journal of molecular sciences · 2025Article
- State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues.Journal of imaging · 2024Review
- VISION - an open-source software for automated multi-dimensional image analysis of cellular biophysics.Journal of cell science · 2024Article
- Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.Lab on a chip · 2024Article
- Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges.Frontiers in pharmacology · 2023Review
- Fluorescence fluctuation-based super-resolution microscopy: Basic concepts for an easy start.Journal of microscopy · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the amount of data generated by imaging techniques has grown rapidly, along with increasing computational power and the development of deep learning algorithms. To address the need for powerful automated image analysis tools for a broad range of applications in the biomedical sciences, the Microscopic Image Analyzer (MIA) was developed. MIA combines a graphical user interface that obviates the need for programming skills with state-of-the-art deep-learning algorithms for segmentation, object detection, and classification. It runs as a standalone, platform-independent application and uses open data formats, which are compatible with commonly used open-source software packages. The software provides a unified interface for easy image labeling, model training, and inference. Furthermore, the software was evaluated in a public competition and performed among the top three for all tested datasets.
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.