743 related articles for article (PubMed ID: 34304370)
41. Lesion Border Detection of Skin Cancer Images Using Deep Fully Convolutional Neural Network with Customized Weights.
Kaur R; Hosseini HG; Sinha R
Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():3035-3038. PubMed ID: 34891883
[TBL] [Abstract][Full Text] [Related]
42. Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms.
Alsaade FW; Aldhyani THH; Al-Adhaileh MH
Comput Math Methods Med; 2021; 2021():9998379. PubMed ID: 34055044
[TBL] [Abstract][Full Text] [Related]
43. A multiple-channel and atrous convolution network for ultrasound image segmentation.
Zhang L; Zhang J; Li Z; Song Y
Med Phys; 2020 Dec; 47(12):6270-6285. PubMed ID: 33007105
[TBL] [Abstract][Full Text] [Related]
44. Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification.
Mahbod A; Schaefer G; Wang C; Dorffner G; Ecker R; Ellinger I
Comput Methods Programs Biomed; 2020 Sep; 193():105475. PubMed ID: 32268255
[TBL] [Abstract][Full Text] [Related]
45. Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding.
Garcia-Arroyo JL; Garcia-Zapirain B
Comput Methods Programs Biomed; 2019 Jan; 168():11-19. PubMed ID: 30527129
[TBL] [Abstract][Full Text] [Related]
46. Melanoma recognition in dermoscopy images using lesion's peripheral region information.
Tajeddin NZ; Asl BM
Comput Methods Programs Biomed; 2018 Sep; 163():143-153. PubMed ID: 30119849
[TBL] [Abstract][Full Text] [Related]
47. Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study.
Maron RC; Hekler A; Krieghoff-Henning E; Schmitt M; Schlager JG; Utikal JS; Brinker TJ
J Med Internet Res; 2021 Mar; 23(3):e21695. PubMed ID: 33764307
[TBL] [Abstract][Full Text] [Related]
48. S
Alam MJ; Mohammad MS; Hossain MAF; Showmik IA; Raihan MS; Ahmed S; Mahmud T
Comput Biol Med; 2022 Nov; 150():106148. PubMed ID: 36252363
[TBL] [Abstract][Full Text] [Related]
49. An End-to-End Multi-Task Deep Learning Framework for Skin Lesion Analysis.
Song L; Lin J; Wang ZJ; Wang H
IEEE J Biomed Health Inform; 2020 Oct; 24(10):2912-2921. PubMed ID: 32071016
[TBL] [Abstract][Full Text] [Related]
50. Deep Convolutional Neural Network for Melanoma Detection using Dermoscopy Images.
R K; H G; R S
Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():1524-1527. PubMed ID: 33018281
[TBL] [Abstract][Full Text] [Related]
51. A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification.
Xie Y; Zhang J; Xia Y; Shen C
IEEE Trans Med Imaging; 2020 Jul; 39(7):2482-2493. PubMed ID: 32070946
[TBL] [Abstract][Full Text] [Related]
52. Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion.
Yang CH; Ren JH; Huang HC; Chuang LY; Chang PY
Comput Intell Neurosci; 2021; 2021():9409508. PubMed ID: 34790232
[TBL] [Abstract][Full Text] [Related]
53. [Application of a parallel branches network based on Transformer for skin melanoma segmentation].
Yi S; Zhang G; He J
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2022 Oct; 39(5):937-944. PubMed ID: 36310482
[TBL] [Abstract][Full Text] [Related]
54. Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma.
Jafari MH; Nasr-Esfahani E; Karimi N; Soroushmehr SMR; Samavi S; Najarian K
Int J Comput Assist Radiol Surg; 2017 Jun; 12(6):1021-1030. PubMed ID: 28342106
[TBL] [Abstract][Full Text] [Related]
55. A Segmentation of Melanocytic Skin Lesions in Dermoscopic and Standard Images Using a Hybrid Two-Stage Approach.
Hwang YN; Seo MJ; Kim SM
Biomed Res Int; 2021; 2021():5562801. PubMed ID: 33880368
[TBL] [Abstract][Full Text] [Related]
56. Ensemble Method of Convolutional Neural Networks with Directed Acyclic Graph Using Dermoscopic Images: Melanoma Detection Application.
Foahom Gouabou AC; Damoiseaux JL; Monnier J; Iguernaissi R; Moudafi A; Merad D
Sensors (Basel); 2021 Jun; 21(12):. PubMed ID: 34200521
[TBL] [Abstract][Full Text] [Related]
57. GA-Net: Ghost convolution adaptive fusion skin lesion segmentation network.
Zhou L; Liang L; Sheng X
Comput Biol Med; 2023 Sep; 164():107273. PubMed ID: 37562327
[TBL] [Abstract][Full Text] [Related]
58. Introducing frequency representation into convolution neural networks for medical image segmentation via twin-Kernel Fourier convolution.
Tang X; Peng J; Zhong B; Li J; Yan Z
Comput Methods Programs Biomed; 2021 Jun; 205():106110. PubMed ID: 33910149
[TBL] [Abstract][Full Text] [Related]
59. Skin lesion image segmentation using Delaunay Triangulation for melanoma detection.
Pennisi A; Bloisi DD; Nardi D; Giampetruzzi AR; Mondino C; Facchiano A
Comput Med Imaging Graph; 2016 Sep; 52():89-103. PubMed ID: 27215953
[TBL] [Abstract][Full Text] [Related]
60. Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification.
Al-Masni MA; Kim DH; Kim TS
Comput Methods Programs Biomed; 2020 Jul; 190():105351. PubMed ID: 32028084
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
