ArticleIEEE transactions on big data2020
Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis.
Article in IEEE transactions on big data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques.Diagnostics (Basel, Switzerland) · 2024Review
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images.Frontiers in bioengineering and biotechnology · 2023Article
- Ten quick tips for deep learning in biology.PLoS computational biology · 2022Article
- Deep autoencoder based domain adaptation for transfer learning.Multimedia tools and applications · 2022Article
- Artificial Intelligence for Autonomous Molecular Design: A Perspective.Molecules (Basel, Switzerland) · 2021Review
- Distant Domain Transfer Learning for Medical Imaging.IEEE journal of biomedical and health informatics · 2021Article
- Modeling multi-species RNA modification through multi-task curriculum learning.Nucleic acids research · 2021Article
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- Deep learning for cellular image analysis.Nature methods · 2019Review
- Predicting gene regulatory interactions based on spatial gene expression data and deep learning.PLoS computational biology · 2019Article
- EEG-based mild depression recognition using convolutional neural network.Medical & biological engineering & computing · 2019Article
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- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
- Does Manual Delineation only Provide the Side Information in CT Prostate Segmentation?Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2017Article
- A neural network multi-task learning approach to biomedical named entity recognition.BMC bioinformatics · 2017Article
- TL-med: A Two-stage transfer learning recognition model for medical images of COVID-19.Biocybernetics and biomedical engineeringArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
A central theme in learning from image data is to develop appropriate representations for the specific task at hand. Thus, a practical challenge is to determine what features are appropriate for specific tasks. For example, in the study of gene expression patterns in
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.