J. Hagerty

Real‐time supervised detection of pink areas in dermoscopic images of melanoma: importance of color shades, texture and location

R. H. Moss, W. Stoecker, R. Kaur, 2015, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

A deep learning approach to detect blood vessels in basal cell carcinoma

W. Stoecker, R. Stanley, J. Hagerty, 2022, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

Deep learning-based dot and globule segmentation with pixel and blob-based metrics for evaluation

W. Stoecker, R. Stanley, J. Hagerty, 2022, Intell. Syst. Appl..

ChimeraNet: U-Net for Hair Detection in Dermoscopic Skin Lesion Images

W. Stoecker, R. Stanley, J. Hagerty, 2022, Journal of Digital Imaging.

Automatic lesion border selection in dermoscopy images using morphology and color features

Ravneet Kaur, Reda Kasmi, R. H. Moss, 2019, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation of Irregular Networks

W. Stoecker, R. Stanley, J. Hagerty, 2023, Cancers.

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

R. Joe Stanley, Peng Guo, Reda Kasmi, 2019, IEEE Journal of Biomedical and Health Informatics.

Fuzzy logic color detection: Blue areas in melanoma dermoscopy images

R. Joe Stanley, William V. Stoecker, Jason R. Hagerty, 2014, Comput. Medical Imaging Graph..

Discrimination of squamous cell carcinoma in situ from seborrheic keratosis by color analysis techniques requires information from scale, scale-crust and surrounding areas in dermoscopy images

Robert W. LeAnder, William V. Stoecker, Jason R. Hagerty, 2012, Comput. Biol. Medicine.

Increasing Melanoma Diagnostic Confidence: Forcing the Convolutional Network to Learn from the Lesion

W. Stoecker, R. Stanley, J. Hagerty, 2023, ArXiv.

Automatic Separation of Basal Cell Carcinoma from Benign Lesions in Dermoscopy Images with Border Thresholding Techniques

R. Joe Stanley, Randy H. Moss, Reda Kasmi, 2017, VISIGRAPP.

Medical Image Processing in the Age of Deep Learning - Is There Still Room for Conventional Medical Image Processing Techniques?

R. Joe Stanley, William V. Stoecker, Jason R. Hagerty, 2017, VISIGRAPP.

Adaptable Ring for Vision-Based Measurements and Shape Analysis

R. Joe Stanley, Randy H. Moss, William V. Stoecker, 2017, IEEE Transactions on Instrumentation and Measurement.

Using Adaptive Thresholding and Skewness Correction to Detect Gray Areas in Melanoma In Situ Images

Giovanna Castellano, R. Joe Stanley, Gianluca Sforza, 2012, IEEE Transactions on Instrumentation and Measurement.

Scholars' Mine Scholars' Mine Using adaptive thresholding and skewness correction to detect Using adaptive thresholding and skewness correction to detect gray areas in melanoma gray areas in melanoma in situ in situ images images

J. Hagerty, 2020 .

Adaptive segmentation of gray areas in dermoscopy images

Giovanna Castellano, R. Joe Stanley, Gianluca Sforza, 2011, 2011 IEEE International Symposium on Medical Measurements and Applications.

Skin Lesion Segmentation in Dermoscopic Images with Noisy Data

R. Stanley, J. Hagerty, W. Van Stoecker, 2023, Journal of Digital Imaging.