ArticleComputational and structural biotechnology journal2024
Exploring simple triplet representation learning.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Compact neural network algorithm for electrocardiogram classification.Physical and engineering sciences in medicine · 2026Article
- AETHER: Video-Based Smartphone Free Flap Monitoring Using Spatial Phase Coherence and Reference-Corrected Augmented Imaging.Computational and structural biotechnology journal · 2026Article
- CIRCA: comprehensible online system in support of chest X-rays-based screening by COVID-19 example.Scientific reports · 2025Article
- Comparative analysis of supervised and self-supervised learning with small and imbalanced medical imaging datasets.Scientific reports · 2025Article
- Leveraging an ensemble of EfficientNetV1 and EfficientNetV2 models for classification and interpretation of breast cancer histopathology images.Scientific reports · 2025Article
- Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma.BMC cancer · 2025Article
- Fusion-driven semi-supervised learning-based lung nodules classification with dual-discriminator and dual-generator generative adversarial network.BMC medical informatics and decision making · 2024Article
- Late feature fusion using neural network with voting classifier for Parkinson's disease detection.BMC medical informatics and decision making · 2024Article
- A multi-slice attention fusion and multi-view personalized fusion lightweight network for Alzheimer's disease diagnosis.BMC medical imaging · 2024Article
- Bone metastasis prediction in non-small-cell lung cancer: primary CT-based radiomics signature and clinical feature.BMC medical imaging · 2024Article
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4 authors.
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Abstract
Fully supervised learning methods necessitate a substantial volume of labelled training instances, a process that is typically both labour-intensive and costly. In the realm of medical image analysis, this issue is further amplified, as annotated medical images are considerably more scarce than their unlabelled counterparts. Consequently, leveraging unlabelled images to extract meaningful underlying knowledge presents a formidable challenge in medical image analysis. This paper introduces a simple triple-view unsupervised representation learning model (SimTrip) combined with a triple-view architecture and loss function, aiming to learn meaningful inherent knowledge efficiently from unlabelled data with small batch size. With the meaningful representation extracted from unlabelled data, our model demonstrates exemplary performance across two medical image datasets. It achieves this using only partial labels and outperforms other state-of-the-art methods. The method we present herein offers a novel paradigm for unsupervised representation learning, establishing a baseline that is poised to inspire the development of more intricate SimTrip-based methods across a spectrum of computer vision applications. Code and user guide are released at https://github.com/JerryRollingUp/SimTripSystem, the system also runs at http://43.131.9.159:5000/.
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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.