ArticleCommunications biology2024
A composite scaling network of EfficientNet for improving spatial domain identification performance.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.Genome biology · 2026Article
- Machine and Deep Learning Reveal Sequence Determinants Encoding Bivalent Histone Modifications.Communications biology · 2026Article
- Design and evaluation of a remote damage control surgery real-time guidance system based on HoloLens 2 in low-speed network environments.Scientific reports · 2026Article
- Explainable Deep Learning Framework for Binary Corrosion Image Classification Using Grad-CAM.Sensors (Basel, Switzerland) · 2025Article
- Explainable convolutional neural network architectures for high-performance taxonomic classification of gasteroid macrofungi.Scientific reports · 2025Article
- The xIV-LDDMM toolkit of image-varifold based technologies for mapping 3D images and spatial-omics across scales.Communications biology · 2025Article
- Attention-Enhanced CNNs and transformers for accurate monkeypox and skin disease detection.Scientific reports · 2025Article
- Spatial omics technology potentially promotes the progress of tumor immunotherapy.British journal of cancer · 2025Review
- Design of a lightweight recognition network for adult locusts and grasshoppers based on deep learning.iScience · 2025Article
- DualNetM: an adaptive dual network framework for inferring functional-oriented markers.BMC biology · 2025Article
- NeXtMD: a new generation of machine learning and deep learning stacked hybrid framework for accurate identification of anti-inflammatory peptides.BMC biology · 2025Article
- GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference.Frontiers in genetics · 2025Article
- A composite scaling network of EfficientNet for improving spatial domain identification performance.Communications biology · 2024Article
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7 authors.
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Abstract
Spatial Transcriptomics leverages gene expression profiling while preserving spatial location and histological images. However, processing the vast and noisy image data in spatial transcriptomics (ST) for precise recognition of spatial domains remains a challenge. In this study, we propose a method of EfNST for recognizing spatial domains, which employs an efficient composite scaling network of EfficientNet to learn multi-scale image features. Compared with other relevant algorithms on six data sets from three sequencing platforms, EfNST exhibits higher accuracy in discerning fine tissue structures, highlighting its strong scalability to data and operational efficiency. Under limited computing resources, the testing results on multiple data sets show that the EfNST algorithm runs faster while maintaining accuracy. The ablation studies of EfNST model demonstrate the significant effectiveness of the EfficientNet. Within the annotated data sets, EfNST showcases the ability to finely identify subregions within tissue structure and discover corresponding marker genes. In the unannotated data sets, EfNST successfully identifies minute regions within complex tissues and elucidated their spatial expression patterns in biological processes. In summary, EfNST presents a novel approach to inferring cellular spatial organization from discrete data spots with significant implications for the exploration of tissue structure and function.
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