Results 1 to 10 of about 1,011,923 (98)

Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes [PDF]

Frontiers in Bioengineering and Biotechnology, 2023
The cytoskeleton is involved during movement, shaping, resilience, and functionality in immune system cells. Biomarkers such as elasticity and adhesion can be promising alternatives to detect the status of cells upon phenotype activation in correlation ...
Hao Wu, Lei Zhang, Banglei Zhao, Wenjie Yang, Massimiliano Galluzzi   +4 more
doaj   +2 more sources

Spatial-frequency feature fusion network for small dataset fine-grained image classification [PDF]

Scientific Reports
In the study of small datasets, obtaining training samples from source categories for learning is a challenge in fine-grained image classification. Based on the fact that fine-grained concepts can be learned with very few samples, only a small number of ...
Yongfei Guo, Bo Li, Wenyue Zhang, Weilong Dong   +3 more
doaj   +2 more sources

AN APPROACH TO RELIABILITY ANALYSIS OF AIRCRAFT SYSTEMS FOR A SMALL DATASET [PDF]

Scientific Journal of Silesian University of Technology. Series Transport, 2023
Data-driven predictive aircraft maintenance approach typically results in lower maintenance costs, avoiding unnecessary preventive maintenance actions and reducing unexpected failures. Information provided by a reliability analysis of aircraft components
Onyedikachi Chioma OKORO, Maksym ZALISKYI, Dmytriiev SERHII, Ibinabo ABULE   +3 more
doaj   +1 more source

Detecting Proximal Caries on Periapical Radiographs Using Convolutional Neural Networks with Different Training Strategies on Small Datasets

Diagnostics, 2022
The present study aimed to evaluate the performance of convolutional neural networks (CNNs) that were trained with small datasets using different strategies in the detection of proximal caries at different levels of severity on periapical radiographs ...
Xiujiao Lin, Dengwei Hong, Dong Zhang, Mingyi Huang, Hao Yu   +4 more
doaj   +1 more source

Does Formula-Driven Supervised Learning Work on Small Datasets?

IEEE Access, 2023
Does formula-driven supervised learning (FDSL) work effectively with fine-tuning on small datasets? Additionally, how many natural images do a network pre-trained with FDSL require to acquire sufficient image features?
Kodai Nakashima, Hirokatsu Kataoka, Yutaka Satoh   +2 more
doaj   +1 more source

Deep learning for calcium segmentation in intravascular ultrasound images

Current Directions in Biomedical Engineering, 2021
Knowing the shape of vascular calcifications is crucial for appropriate planning and conductance of percutaneous coronary interventions. The clinical workflow can therefore benefit from automatic segmentation of calcified plaques in intravascular ...
Bargsten Lennart, Riedl Katharina A., Wissel Tobias, Brunner Fabian J., Schaefers Klaus, Grass Michael, Blankenberg Stefan, Seiffert Moritz, Schlaefer Alexander   +8 more
doaj   +1 more source

Cost-sensitive regression learning on small dataset through intra-cluster product favoured feature selection

Connection Science, 2022
Massive regression and forecasting tasks are generally cost-sensitive regression learning problems with asymmetric costs between over-prediction and under-prediction.
Fangfang Xu, Huan Zhao, Weihua Zhou, Yun Zhou   +3 more
doaj   +1 more source

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

IEEE Access, 2020
The main obstacle to image augmentation with Generative Adversarial Networks (GANs) is the need for a large amount of training data, but this is difficult for small datasets like Environmental Microorganisms (EMs). EM image analysis plays a vital role in
Hao Xu, Chen Li, Md Mamunur Rahaman, Yudong Yao, Zihan Li, Jinghua Zhang, Frank Kulwa, Xin Zhao, Shouliang Qi, Yueyang Teng   +9 more
doaj   +1 more source

Evaluating Knowledge Transfer in the Neural Network for Medical Images

IEEE Access, 2023
The performance of deep learning models, such as convolutional neural networks (CNN)s, is highly dependent on the size of the training dataset. Consequently, it can be challenging to achieve satisfactory performance when training models from scratch in ...
S. Akbarian, L. Seyyed-Kalantari, F. Khalvati, E. Dolatabadi   +3 more
doaj   +1 more source

Pruning of Network Filters for Small Dataset

IEEE Access, 2020
Many existing object detection networks achieve outstanding accuracy in some open-source datasets, which usually contain a large number of images and target categories.
Zhuang Li, Lihong Xu, Shuwei Zhu
doaj   +1 more source