BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

304 related articles for article (PubMed ID: 15678615)

  • 1. Automated segmentation of routinely hematoxylin-eosin-stained microscopic images by combining support vector machine clustering and active contour models.
    Glotsos D; Spyridonos P; Cavouras D; Ravazoula P; Dadioti PA; Nikiforidis G
    Anal Quant Cytol Histol; 2004 Dec; 26(6):331-40. PubMed ID: 15678615
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Automated cell nuclear segmentation in color images of hematoxylin and eosin-stained breast biopsy.
    Latson L; Sebek B; Powell KA
    Anal Quant Cytol Histol; 2003 Dec; 25(6):321-31. PubMed ID: 14714298
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Using cell nuclei features to detect colon cancer tissue in hematoxylin and eosin stained slides.
    Jørgensen AS; Rasmussen AM; Andersen NKM; Andersen SK; Emborg J; Røge R; Østergaard LR
    Cytometry A; 2017 Aug; 91(8):785-793. PubMed ID: 28727286
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Expectation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR): application to lymphocyte segmentation on breast cancer histopathology.
    Fatakdawala H; Xu J; Basavanhally A; Bhanot G; Ganesan S; Feldman M; Tomaszewski JE; Madabhushi A
    IEEE Trans Biomed Eng; 2010 Jul; 57(7):1676-89. PubMed ID: 20172780
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Computer-based grading of haematoxylin-eosin stained tissue sections of urinary bladder carcinomas.
    Spyridonos P; Ravazoula P; Cavouras D; Berberidis K; Nikiforidis G
    Med Inform Internet Med; 2001; 26(3):179-90. PubMed ID: 11706928
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Computer-based malignancy grading of astrocytomas employing a support vector machine classifier, the WHO grading system and the regular hematoxylin-eosin diagnostic staining procedure.
    Glotsos D; Spyridonos P; Petalas P; Cavouras D; Ravazoula P; Dadioti PA; Lekka I; Nikiforidis G
    Anal Quant Cytol Histol; 2004 Apr; 26(2):77-83. PubMed ID: 15131894
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Segmentation of Heavily Clustered Nuclei from Histopathological Images.
    Abdolhoseini M; Kluge MG; Walker FR; Johnson SJ
    Sci Rep; 2019 Mar; 9(1):4551. PubMed ID: 30872619
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis.
    Rana A; Lowe A; Lithgow M; Horback K; Janovitz T; Da Silva A; Tsai H; Shanmugam V; Bayat A; Shah P
    JAMA Netw Open; 2020 May; 3(5):e205111. PubMed ID: 32432709
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining.
    Zhang L; Kong H; Ting Chin C; Liu S; Fan X; Wang T; Chen S
    Cytometry A; 2014 Mar; 85(3):214-30. PubMed ID: 24376056
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images.
    Salvi M; Molinari F
    Biomed Eng Online; 2018 Jun; 17(1):89. PubMed ID: 29925379
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Detection of malignant melanoma in H&E-stained images using deep learning techniques.
    Alheejawi S; Berendt R; Jha N; Maity SP; Mandal M
    Tissue Cell; 2021 Dec; 73():101659. PubMed ID: 34634635
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Segmentation of HE-stained meningioma pathological images based on pseudo-labels.
    Wu C; Zhong J; Lin L; Chen Y; Xue Y; Shi P
    PLoS One; 2022; 17(2):e0263006. PubMed ID: 35120175
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A novel method for tissue segmentation in high-resolution H&E-stained histopathological whole-slide images.
    Kleczek P; Jaworek-Korjakowska J; Gorgon M
    Comput Med Imaging Graph; 2020 Jan; 79():101686. PubMed ID: 31816574
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Segmentation of transitional cell carcinoma nuclei by nonsupervised thresholding in different color spaces.
    Pavlopoulos PM; Zimeras S; Kavantzas N; Korkolopoulou P; Agapitos E; Patsouris E
    Anal Quant Cytol Histol; 2007 Aug; 29(4):271-8. PubMed ID: 17879636
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin.
    Haggerty JM; Wang XN; Dickinson A; O'Malley CJ; Martin EB
    BMC Med Imaging; 2014 Feb; 14():7. PubMed ID: 24521154
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Tumor grading model employing geometric analysis of histopathological images with characteristic nuclei dictionary.
    Brindha V; Jayashree P; Karthik P; Manikandan P
    Comput Biol Med; 2022 Oct; 149():106008. PubMed ID: 36030720
    [TBL] [Abstract][Full Text] [Related]  

  • 17. CellViT: Vision Transformers for precise cell segmentation and classification.
    Hörst F; Rempe M; Heine L; Seibold C; Keyl J; Baldini G; Ugurel S; Siveke J; Grünwald B; Egger J; Kleesiek J
    Med Image Anal; 2024 May; 94():103143. PubMed ID: 38507894
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review.
    Chen JM; Li Y; Xu J; Gong L; Wang LW; Liu WL; Liu J
    Tumour Biol; 2017 Mar; 39(3):1010428317694550. PubMed ID: 28347240
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Charisma: an integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting.
    Janssens T; Antanas L; Derde S; Vanhorebeek I; Van den Berghe G; Güiza Grandas F
    Med Image Anal; 2013 Dec; 17(8):1206-19. PubMed ID: 24012925
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An algorithm for automatic tracking of nuclear boundaries.
    Xiao J; Christen R; Minimo C; Bartels PH; Bibbo M
    Anal Quant Cytol Histol; 1994 Aug; 16(4):240-6. PubMed ID: 7945699
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.