ArticleSensors (Basel, Switzerland)2021
A Convolutional Autoencoder Topology for Classification in High-Dimensional Noisy Image Datasets.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Hyperbolic Prototype-Residual Autoencoder for Interpretable Generative Latent Organization.Sensors (Basel, Switzerland) · 2026Article
- Sparseness facilitates image encoding across visuo-frontal networks in freely moving macaque.Nature communications · 2026Article
- Multi-stream deep learning framework integrating images and feature representations to predict mild cognitive impairment using the rey complex figure test.Scientific reports · 2026Article
- An Improved Capsule Network for Image Classification Using Multi-Scale Feature Extraction.Journal of imaging · 2025Article
- A Deep Learning-Based Model Approach for Quantitative Analysis of Cell Chemotaxis in a Microfluidic Chip.Sensors (Basel, Switzerland) · 2025Article
- Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning.NPJ precision oncology · 2025Article
- A cost-effectiveness analysis of an integrated clinical-radiogenomic screening program for the identification of BRCA 1/2 carriers (e-PROBE study).Scientific reports · 2024Article
- Multi-Type Missing Imputation of Time-Series Power Equipment Monitoring Data Based on Moving Average Filter-Asymmetric Denoising Autoencoder.Sensors (Basel, Switzerland) · 2023Article
- Information maximization-based clustering of histopathology images using deep learning.PLOS digital health · 2023Article
- Explainable Image Similarity: Integrating Siamese Networks and Grad-CAM.Journal of imaging · 2023Article
- Special Issue on Machine Learning and AI for Sensors.Sensors (Basel, Switzerland) · 2023Article
- Multi-task deep autoencoder to predict Alzheimer's disease progression using temporal DNA methylation data in peripheral blood.Computational and structural biotechnology journal · 2022Article
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3 authors.
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Abstract
Deep convolutional neural networks have shown remarkable performance in the image classification domain. However, Deep Learning models are vulnerable to noise and redundant information encapsulated into the high-dimensional raw input images, leading to unstable and unreliable predictions. Autoencoders constitute an unsupervised dimensionality reduction technique, proven to filter out noise and redundant information and create robust and stable feature representations. In this work, in order to resolve the problem of DL models' vulnerability, we propose a convolutional autoencoder topological model for compressing and filtering out noise and redundant information from initial high dimensionality input images and then feeding this compressed output into convolutional neural networks. Our results reveal the efficiency of the proposed approach, leading to a significant performance improvement compared to Deep Learning models trained with the initial raw images.
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