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D-Index & Metrics

Computer Science

D-Index
42
Citations
7551
World Ranking
8373
National Ranking
3587

Overview

William V. Stoecker is affiliated with the Missouri University of Science and Technology in the United States. Their research primarily focuses on the intersection of medicine and computer science, with a special emphasis on oncology, epidemiology, and artificial intelligence.

The scientist's work spans several subfields including radiology, nuclear medicine and imaging, and computer vision and pattern recognition. Key research topics covered in their publications include:

  • AI in cancer detection
  • Cutaneous melanoma detection and management
  • Nonmelanoma skin cancer studies
  • Cervical cancer and HPV research
  • Radiomics and machine learning in medical imaging
  • Genetic and rare skin diseases
  • Cell image analysis techniques

William V. Stoecker has contributed to a number of research papers in prominent scientific venues. Recent papers include:

  • "ChimeraNet: U-Net for Hair Detection in Dermoscopic Skin Lesion Images," 2022, published in Journal of Digital Imaging
  • "Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation of Irregular Networks," 2023, published in Cancers
  • "EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images," 2020, published in Journal of Pathology Informatics
  • "DeepCIN: Attention-Based Cervical histology Image Classification with Sequential Feature Modeling for Pathologist-Level Accuracy," 2020, published in Journal of Pathology Informatics
  • "Automated Cervical Digitized Histology Whole-Slide Image Analysis Toolbox," 2021, published in Journal of Pathology Informatics

The scientist frequently publishes in venues such as Zenodo (CERN European Organization for Nuclear Research), Journal of Pathology Informatics, Journal of Imaging Informatics in Medicine, Journal of Digital Imaging, and Skin Research and Technology.

Frequent co-authors collaborating with William V. Stoecker include Jason Hagerty, Norsang Lama, Anand K. Nambisan, R. Joe Stanley, and Akanksha Maurya. These collaborations have contributed to multiple papers across the scientist's fields of interest, reflecting continued research partnerships.

Best Publications

  • A Methodological Approach to the Classification of Dermoscopy Images

    M. Emre Celebi;Hassan A. Kingravi;Bakhtiyar Uddin;Hitoshi Iyatomi

  • Lesion border detection in dermoscopy images.

    M.Emre Celebi;Hitoshi Iyatomi;Gerald Schaefer;William V. Stoecker

  • Neural network diagnosis of malignant melanoma from color images

    F. Ercal;A. Chawla;W.V. Stoecker;Hsi-Chieh Lee

  • Border detection in dermoscopy images using statistical region merging.

    M. Emre Celebi;Hassan A. Kingravi;Hitoshi Iyatomi;Y. Alp Aslandogan

  • Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes.

    Bulent Erkol;Randy Hays Moss;R. Joe Stanley;William V. Stoecker

  • Unsupervised color image segmentation: with application to skin tumor borders

    G.A. Hance;S.E. Umbaugh;R.H. Moss;W.V. Stoecker

  • Accuracy in melanoma detection: A 10-year multicenter survey

    Giuseppe Argenziano;Lorenzo Cerroni;Iris Zalaudek;Stefania Staibano

  • Unsupervised border detection in dermoscopy images

    M. Emre Celebi;Y. Alp Aslandogan;William V. Stoecker;Hitoshi Iyatomi

  • Automatic detection of blue-white veil and related structures in dermoscopy images

    M. Emre Celebi;Hitoshi Iyatomi;William V. Stoecker;Randy H. Moss

  • Deep Learning and Handcrafted Method Fusion: Higher Diagnostic Accuracy for Melanoma Dermoscopy Images

    Jason R. Hagerty;R. Joe Stanley;Haidar A. Almubarak;Norsang Lama

  • Independent Histogram Pursuit for Segmentation of Skin Lesions

    D.D. Gomez;C. Butakoff;B.K. Ersboll;W. Stoecker

  • Automatic color segmentation algorithms-with application to skin tumor feature identification

    S.E. Umbaugh;R.H. Moss;W.V. Stoecker;G.A. Hance

  • Automatic color segmentation of images with application to detection of variegated coloring in skin tumors

    S.E. Umbaugh;R.H. Moss;W.V. Stoecker

  • Automatic detection of asymmetry in skin tumors.

    William V. Stoecker;William Weiling Li;Randy Hays Moss

  • A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images.

    R. Joe Stanley;William V. Stoecker;Randy Hays Moss

  • Skin lesion classification using relative color features

    Yue (Iris) Cheng;Ragavendar Swamisai;Scott E. Umbaugh;Randy H. Moss

  • Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.

    Sudhir Sornapudi;Ronald Joe Stanley;William V Stoecker;Haidar Almubarak

  • Differentiation among basal cell carcinoma, benign lesions, and normal skin using electric impedance

    D.G. Beetner;S. Kapoor;S. Manjunath;Xiangyang Zhou

  • Detection of granularity in dermoscopy images of malignant melanoma using color and texture features.

    William V. Stoecker;Mark Wronkiewiecz;Raeed H. Chowdhury;R. Joe Stanley

  • A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images.

    R.Joe Stanley;Randy Hays Moss;William Van Stoecker;Chetna Aggarwal

  • Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color.

    William V. Stoecker;Kapil Gupta;R. Joe Stanley;Randy Hays Moss

Frequent Co-Authors

Randy Hays Moss
Randy Hays Moss Missouri University of Science and Technology
M. Emre Celebi
M. Emre Celebi University of Central Arkansas
Sameer Antani
Sameer Antani National Institutes of Health
George R. Thoma
George R. Thoma National Institutes of Health
Gerald Schaefer
Gerald Schaefer Loughborough University
Reza Zoughi
Reza Zoughi Iowa State University

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