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The following term was not found in PubMed: Kiatisak
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MBFFNet: Multi-Branch Feature Fusion Network for Colonoscopy.
Su H, Lin B, Huang X, Li J, Jiang K, Duan X. Su H, et al. Front Bioeng Biotechnol. 2021 Jul 14;9:696251. doi: 10.3389/fbioe.2021.696251. eCollection 2021. Front Bioeng Biotechnol. 2021. PMID: 34336808 Free PMC article.
The actual segmentation effect of MBFFNet is only lower than that of PraNet, the number of parameters is 78.27% of that of PraNet, and the flop count is 0.23% that of PraNet. ...
The actual segmentation effect of MBFFNet is only lower than that of PraNet, the number of parameters is 78.27% of that of PraNet …
Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images.
Inoda S, Takahashi H, Yamagata H, Hisadome Y, Kondo Y, Tampo H, Sakamoto S, Katada Y, Kurihara T, Kawashima H, Yanagi Y. Inoda S, et al. Sci Rep. 2022 Dec 17;12(1):21826. doi: 10.1038/s41598-022-25894-9. Sci Rep. 2022. PMID: 36528737 Free PMC article.
We herein propose a PraNet-based deep-learning model for estimating the size of non-perfusion area (NPA) in pseudo-color fundus photos from an ultra-wide-field (UWF) image. ...In conclusion, we developed an AI model capable of estimating NPA size from only an UWF image wit …
We herein propose a PraNet-based deep-learning model for estimating the size of non-perfusion area (NPA) in pseudo-color fundus photo …
PRAPNet: A Parallel Residual Atrous Pyramid Network for Polyp Segmentation.
Han J, Xu C, An Z, Qian K, Tan W, Wang D, Fang Q. Han J, et al. Sensors (Basel). 2022 Jun 21;22(13):4658. doi: 10.3390/s22134658. Sensors (Basel). 2022. PMID: 35808154 Free PMC article.
The experimental results outperformed the scores achieved by the seven classical segmentation network models (U-Net, U-Net++, ResUNet++, praNet, CaraNet, SFFormer-L, TransFuse-L)....
The experimental results outperformed the scores achieved by the seven classical segmentation network models (U-Net, U-Net++, ResUNet++, …
CTNet: Contrastive Transformer Network for Polyp Segmentation.
Xiao B, Hu J, Li W, Pun CM, Bi X. Xiao B, et al. IEEE Trans Cybern. 2024 Mar 18;PP. doi: 10.1109/TCYB.2024.3368154. Online ahead of print. IEEE Trans Cybern. 2024. PMID: 38470573
Without bells and whistles, CTNet yields significant gains of 2.3%, 3.7%, 3.7%, 18.2%, and 10.1% over classical method PraNet on Kvasir-SEG, CVC-ClinicDB, Endoscene, ETIS-LaribPolypDB, and CVC-ColonDB respectively. ...
Without bells and whistles, CTNet yields significant gains of 2.3%, 3.7%, 3.7%, 18.2%, and 10.1% over classical method PraNet on Kvas …
A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution.
Safarov S, Whangbo TK. Safarov S, et al. Sensors (Basel). 2021 Feb 19;21(4):1441. doi: 10.3390/s21041441. Sensors (Basel). 2021. PMID: 33669539 Free PMC article.
A-DenseUNet achieved a 90% Dice coefficient score on the Kvasir-SEG dataset and a 91% Dice coefficient score on the CVC-612 dataset, both of which were higher than the scores of other deep learning models such as UNet++, ResUNet, U-Net, PraNet, and ResUNet++ for segmenting …
A-DenseUNet achieved a 90% Dice coefficient score on the Kvasir-SEG dataset and a 91% Dice coefficient score on the CVC-612 dataset, both of …
Dual-branch multi-information aggregation network with transformer and convolution for polyp segmentation.
Zhang W, Lu F, Su H, Hu Y. Zhang W, et al. Comput Biol Med. 2024 Jan;168:107760. doi: 10.1016/j.compbiomed.2023.107760. Epub 2023 Nov 30. Comput Biol Med. 2024. PMID: 38064849
Especially, we achieve 94.12% mean Dice on CVC-ClinicDB dataset which is 4.22% improvement compared to the previous state-of-the-art method PraNet. Compared with SOTA algorithms, DBMIA-Net has a better fitting ability and stronger generalization ability....
Especially, we achieve 94.12% mean Dice on CVC-ClinicDB dataset which is 4.22% improvement compared to the previous state-of-the-art method …