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Page 1
ChimeraNet: U-Net for Hair Detection in Dermoscopic Skin Lesion Images.
Lama N, Kasmi R, Hagerty JR, Stanley RJ, Young R, Miinch J, Nepal J, Nambisan A, Stoecker WV. Lama N, et al. J Digit Imaging. 2023 Apr;36(2):526-535. doi: 10.1007/s10278-022-00740-6. Epub 2022 Nov 16. J Digit Imaging. 2023. PMID: 36385676 Free PMC article.
Further evaluated on 25 additional test images, the technique yields state-of-the-art accuracy compared to 8 previously reported classical techniques. ...
Further evaluated on 25 additional test images, the technique yields state-of-the-art accuracy compared to 8 previously reported clas …
EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images.
Sornapudi S, Hagerty J, Stanley RJ, Stoecker WV, Long R, Antani S, Thoma G, Zuna R, Frazier SR. Sornapudi S, et al. J Pathol Inform. 2020 Mar 30;11:10. doi: 10.4103/jpi.jpi_53_19. eCollection 2020. J Pathol Inform. 2020. PMID: 32477616 Free PMC article.
CONCLUSIONS: EpithNet yields better epithelial segmentation results than state-of-the-art benchmark methods....
CONCLUSIONS: EpithNet yields better epithelial segmentation results than state-of-the-art benchmark methods....
Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.
Sornapudi S, Stanley RJ, Stoecker WV, Almubarak H, Long R, Antani S, Thoma G, Zuna R, Frazier SR. Sornapudi S, et al. J Pathol Inform. 2018 Mar 5;9:5. doi: 10.4103/jpi.jpi_74_17. eCollection 2018. J Pathol Inform. 2018. PMID: 29619277 Free PMC article.
CONCLUSIONS: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods....
CONCLUSIONS: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison …
Hybrid Topological Data Analysis and Deep Learning for Basal Cell Carcinoma Diagnosis.
Maurya A, Stanley RJ, Lama N, Nambisan AK, Patel G, Saeed D, Swinfard S, Smith C, Jagannathan S, Hagerty JR, Stoecker WV. Maurya A, et al. J Imaging Inform Med. 2024 Feb;37(1):92-106. doi: 10.1007/s10278-023-00924-8. Epub 2024 Jan 12. J Imaging Inform Med. 2024. PMID: 38343238 Free PMC article.
Persistence homology (a TDA technique) is implemented to extract topological features from automatically segmented telangiectasia as well as skin lesions, and DL features are generated by fine-tuning a pre-trained EfficientNet-B5 model. The final hybrid TDA-DL model achieves stat …
Persistence homology (a TDA technique) is implemented to extract topological features from automatically segmented telangiectasia as well as …
Thresholding methods for lesion segmentation of basal cell carcinoma in dermoscopy images.
Kaur R, LeAnder R, Mishra NK, Hagerty JR, Kasmi R, Stanley RJ, Celebi ME, Stoecker WV. Kaur R, et al. Skin Res Technol. 2017 Aug;23(3):416-428. doi: 10.1111/srt.12352. Epub 2016 Nov 28. Skin Res Technol. 2017. PMID: 27892649
RESULTS: On training/test sets of 305 and 34 BCC images, respectively, five new techniques outperform two state-of-the-art methods used in segmentation of melanomas, based on the new error metrics. ...
RESULTS: On training/test sets of 305 and 34 BCC images, respectively, five new techniques outperform two state-of-the-art methods us …
Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features.
Maurya A, Stanley RJ, Aradhyula HY, Lama N, Nambisan AK, Patel G, Saeed D, Swinfard S, Smith C, Jagannathan S, Hagerty JR, Stoecker WV. Maurya A, et al. J Imaging Inform Med. 2024 Feb 8. doi: 10.1007/s10278-024-00969-3. Online ahead of print. J Imaging Inform Med. 2024. PMID: 38332404
Another novel approach to feature finding with weak annotations through the examination of the surrounding areas of telangiectasia is offered in this study. The experimental results show state-of-the-art accuracy and precision in the diagnosis of BCC, compared to three ben …
Another novel approach to feature finding with weak annotations through the examination of the surrounding areas of telangiectasia is offere …
Border detection in dermoscopy images using statistical region merging.
Celebi ME, Kingravi HA, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH, Malters JM, Grichnik JM, Marghoob AA, Rabinovitz HS, Menzies SW. Celebi ME, et al. Skin Res Technol. 2008 Aug;14(3):347-53. doi: 10.1111/j.1600-0846.2008.00301.x. Skin Res Technol. 2008. PMID: 19159382 Free PMC article.
The border detection error is quantified by a metric in which three sets of dermatologist-determined borders are used as the ground-truth. The proposed method is compared with four state-of-the-art automated methods (orientation-sensitive fuzzy c-means, dermatologist-like …
The border detection error is quantified by a metric in which three sets of dermatologist-determined borders are used as the ground-truth. T …