126 related articles for article (PubMed ID: 38227593)
1. Rapid dataset generation methods for stacked construction solid waste based on machine vision and deep learning.
Ji T; Li J; Fang H; Zhang R; Yang J; Fan L
PLoS One; 2024; 19(1):e0296666. PubMed ID: 38227593
[TBL] [Abstract][Full Text] [Related]
2. A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica.
Calderon-Ramirez S; Murillo-Hernandez D; Rojas-Salazar K; Elizondo D; Yang S; Moemeni A; Molina-Cabello M
Med Biol Eng Comput; 2022 Apr; 60(4):1159-1175. PubMed ID: 35239108
[TBL] [Abstract][Full Text] [Related]
3. Using pseudo-labeling to improve performance of deep neural networks for animal identification.
Ferreira REP; Lee YJ; Dórea JRR
Sci Rep; 2023 Aug; 13(1):13875. PubMed ID: 37620446
[TBL] [Abstract][Full Text] [Related]
4. An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation.
Namoun A; Hussein BR; Tufail A; Alrehaili A; Syed TA; BenRhouma O
Sensors (Basel); 2022 May; 22(9):. PubMed ID: 35591195
[TBL] [Abstract][Full Text] [Related]
5. Airborne pollen grain detection from partially labelled data utilising semi-supervised learning.
Jin B; Milling M; Plaza MP; Brunner JO; Traidl-Hoffmann C; Schuller BW; Damialis A
Sci Total Environ; 2023 Sep; 891():164295. PubMed ID: 37211136
[TBL] [Abstract][Full Text] [Related]
6. Real-time construction demolition waste detection using state-of-the-art deep learning methods; single-stage vs two-stage detectors.
Demetriou D; Mavromatidis P; Robert PM; Papadopoulos H; Petrou MF; Nicolaides D
Waste Manag; 2023 Jul; 167():194-203. PubMed ID: 37269583
[TBL] [Abstract][Full Text] [Related]
7. A modality-collaborative convolution and transformer hybrid network for unpaired multi-modal medical image segmentation with limited annotations.
Liu H; Zhuang Y; Song E; Xu X; Ma G; Cetinkaya C; Hung CC
Med Phys; 2023 Sep; 50(9):5460-5478. PubMed ID: 36864700
[TBL] [Abstract][Full Text] [Related]
8. Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification.
Marini N; Otálora S; Müller H; Atzori M
Med Image Anal; 2021 Oct; 73():102165. PubMed ID: 34303169
[TBL] [Abstract][Full Text] [Related]
9. Computer vision for solid waste sorting: A critical review of academic research.
Lu W; Chen J
Waste Manag; 2022 Apr; 142():29-43. PubMed ID: 35172271
[TBL] [Abstract][Full Text] [Related]
10. Multi-modal deep learning networks for RGB-D pavement waste detection and recognition.
Li Y; Zhang X
Waste Manag; 2024 Apr; 177():125-134. PubMed ID: 38325013
[TBL] [Abstract][Full Text] [Related]
11. RGB-D fusion models for construction and demolition waste detection.
Li J; Fang H; Fan L; Yang J; Ji T; Chen Q
Waste Manag; 2022 Feb; 139():96-104. PubMed ID: 34954663
[TBL] [Abstract][Full Text] [Related]
12. Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone.
Cruciani F; Cleland I; Nugent C; McCullagh P; Synnes K; Hallberg J
Sensors (Basel); 2018 Jul; 18(7):. PubMed ID: 29987218
[TBL] [Abstract][Full Text] [Related]
13. Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation.
Cuypers S; Bassier M; Vergauwen M
Sensors (Basel); 2021 Aug; 21(16):. PubMed ID: 34450870
[TBL] [Abstract][Full Text] [Related]
14. BertSRC: transformer-based semantic relation classification.
Lee Y; Son J; Song M
BMC Med Inform Decis Mak; 2022 Sep; 22(1):234. PubMed ID: 36068535
[TBL] [Abstract][Full Text] [Related]
15. Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation.
Chaitanya K; Erdil E; Karani N; Konukoglu E
Med Image Anal; 2023 Jul; 87():102792. PubMed ID: 37054649
[TBL] [Abstract][Full Text] [Related]
16. deepNIR: Datasets for Generating Synthetic NIR Images and Improved Fruit Detection System Using Deep Learning Techniques.
Sa I; Lim JY; Ahn HS; MacDonald B
Sensors (Basel); 2022 Jun; 22(13):. PubMed ID: 35808218
[TBL] [Abstract][Full Text] [Related]
17. The Challenge of Data Annotation in Deep Learning-A Case Study on Whole Plant Corn Silage.
Rasmussen CB; Kirk K; Moeslund TB
Sensors (Basel); 2022 Feb; 22(4):. PubMed ID: 35214497
[TBL] [Abstract][Full Text] [Related]
18. Deep convolutional neural networks for construction and demolition waste classification: VGGNet structures, cyclical learning rate, and knowledge transfer.
Lin K; Zhou T; Gao X; Li Z; Duan H; Wu H; Lu G; Zhao Y
J Environ Manage; 2022 Sep; 318():115501. PubMed ID: 35717691
[TBL] [Abstract][Full Text] [Related]
19. On the objectivity, reliability, and validity of deep learning enabled bioimage analyses.
Segebarth D; Griebel M; Stein N; von Collenberg CR; Martin C; Fiedler D; Comeras LB; Sah A; Schoeffler V; Lüffe T; Dürr A; Gupta R; Sasi M; Lillesaar C; Lange MD; Tasan RO; Singewald N; Pape HC; Flath CM; Blum R
Elife; 2020 Oct; 9():. PubMed ID: 33074102
[TBL] [Abstract][Full Text] [Related]
20. Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging.
Wolf D; Payer T; Lisson CS; Lisson CG; Beer M; Götz M; Ropinski T
Sci Rep; 2023 Nov; 13(1):20260. PubMed ID: 37985685
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]