BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

125 related articles for article (PubMed ID: 32835893)

  • 1. Fast fully automatic skin lesions segmentation probabilistic with Parzen window.
    Chagas JVSD; Ivo RF; Guimarães MT; de A Rodrigues D; de S Rebouças E; Rebouças Filho PP
    Comput Med Imaging Graph; 2020 Oct; 85():101774. PubMed ID: 32835893
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Efficient skin lesion segmentation using separable-Unet with stochastic weight averaging.
    Tang P; Liang Q; Yan X; Xiang S; Sun W; Zhang D; Coppola G
    Comput Methods Programs Biomed; 2019 Sep; 178():289-301. PubMed ID: 31416556
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks.
    Al-Masni MA; Al-Antari MA; Choi MT; Han SM; Kim TS
    Comput Methods Programs Biomed; 2018 Aug; 162():221-231. PubMed ID: 29903489
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images.
    Kaur R; GholamHosseini H; Sinha R; Lindén M
    BMC Med Imaging; 2022 May; 22(1):103. PubMed ID: 35644612
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Segmentation of dermoscopy images based on deformable 3D convolution and ResU-NeXt +.
    Zhao C; Shuai R; Ma L; Liu W; Wu M
    Med Biol Eng Comput; 2021 Sep; 59(9):1815-1832. PubMed ID: 34304370
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Melanoma segmentation using deep learning with test-time augmentations and conditional random fields.
    Ashraf H; Waris A; Ghafoor MF; Gilani SO; Niazi IK
    Sci Rep; 2022 Mar; 12(1):3948. PubMed ID: 35273282
    [TBL] [Abstract][Full Text] [Related]  

  • 9. DSNet: Automatic dermoscopic skin lesion segmentation.
    Hasan MK; Dahal L; Samarakoon PN; Tushar FI; Martí R
    Comput Biol Med; 2020 May; 120():103738. PubMed ID: 32421644
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks.
    Bi L; Kim J; Ahn E; Kumar A; Fulham M; Feng D
    IEEE Trans Biomed Eng; 2017 Sep; 64(9):2065-2074. PubMed ID: 28600236
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Melanoma Skin Cancer Detection Method Based on Adaptive Principal Curvature, Colour Normalisation and Feature Extraction with the ABCD Rule.
    Thanh DNH; Prasath VBS; Hieu LM; Hien NN
    J Digit Imaging; 2020 Jun; 33(3):574-585. PubMed ID: 31848895
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Dynamically aggregating MLPs and CNNs for skin lesion segmentation with geometry regularization.
    Qin C; Zheng B; Zeng J; Chen Z; Zhai Y; Genovese A; Piuri V; Scotti F
    Comput Methods Programs Biomed; 2023 Aug; 238():107601. PubMed ID: 37210926
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification.
    Khan MA; Akram T; Sharif M; Shahzad A; Aurangzeb K; Alhussein M; Haider SI; Altamrah A
    BMC Cancer; 2018 Jun; 18(1):638. PubMed ID: 29871593
    [TBL] [Abstract][Full Text] [Related]  

  • 16. New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation.
    Masoud Abdulhamid IA; Sahiner A; Rahebi J
    Biomed Res Int; 2020; 2020():5345923. PubMed ID: 32351994
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Machine learning based skin lesion segmentation method with novel borders and hair removal techniques.
    Rehman M; Ali M; Obayya M; Asghar J; Hussain L; K Nour M; Negm N; Mustafa Hilal A
    PLoS One; 2022; 17(11):e0275781. PubMed ID: 36355845
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Computer-Aided Diagnosis Algorithm for Classification of Malignant Melanoma Using Deep Neural Networks.
    Kim CI; Hwang SM; Park EB; Won CH; Lee JH
    Sensors (Basel); 2021 Aug; 21(16):. PubMed ID: 34450993
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture.
    Attia M; Hossny M; Zhou H; Nahavandi S; Asadi H; Yazdabadi A
    Comput Methods Programs Biomed; 2019 Aug; 177():17-30. PubMed ID: 31319945
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network.
    Zafar K; Gilani SO; Waris A; Ahmed A; Jamil M; Khan MN; Sohail Kashif A
    Sensors (Basel); 2020 Mar; 20(6):. PubMed ID: 32183041
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.