Nuclei graph local features for basal cell carcinoma classification in whole slide images

2017 ◽  
Author(s):  
David Romo-Bucheli ◽  
Germán Corredor ◽  
Juan D. García-Arteaga ◽  
Viviana Arias ◽  
Eduardo Romero
Keyword(s):  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Local Features ◽  
Whole Slide Images

Dermatoscopy of basal cell carcinoma: Morphologic variability of global and local features and accuracy of diagnosis

2010 ◽  
Vol 62 (1) ◽  
pp. 67-75 ◽  
Author(s):  
Davide Altamura ◽  
Scott W. Menzies ◽  
Giuseppe Argenziano ◽  
Iris Zalaudek ◽  
H. Peter Soyer ◽  
...  
Keyword(s):  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Local Features ◽  
Global And Local

scholarly journals Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma

2021 ◽  
Vol 7 (4) ◽  
pp. 71
Author(s):  
Jean Le’Clerc Arrastia ◽  
Nick Heilenkötter ◽  
Daniel Otero Baguer ◽  
Lena Hauberg-Lotte ◽  
Tobias Boskamp ◽  
...  
Keyword(s):  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Resection Margins ◽  
Tissue Sections ◽  
Tumor Tissues ◽  
Critical Regions ◽  
Whole Slide Images

Accurate and fast assessment of resection margins is an essential part of a dermatopathologist’s clinical routine. In this work, we successfully develop a deep learning method to assist the dermatopathologists by marking critical regions that have a high probability of exhibiting pathological features in whole slide images (WSI). We focus on detecting basal cell carcinoma (BCC) through semantic segmentation using several models based on the UNet architecture. The study includes 650 WSI with 3443 tissue sections in total. Two clinical dermatopathologists annotated the data, marking tumor tissues’ exact location on 100 WSI. The rest of the data, with ground-truth sectionwise labels, are used to further validate and test the models. We analyze two different encoders for the first part of the UNet network and two additional training strategies: (a) deep supervision, (b) linear combination of decoder outputs, and obtain some interpretations about what the network’s decoder does in each case. The best model achieves over 96%, accuracy, sensitivity, and specificity on the Test set.

Ultrastructure of Mouse Basal Cell Carcinoma Exhibiting Sebaceous Differentiation

1980 ◽  
Vol 38 ◽  
pp. 738-739
Author(s):  
Victoria L. Wade ◽  
Winslow G. Sheldon ◽  
James W. Townsend ◽  
William Allaben
Keyword(s):  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Topical Application ◽  
Basal Cell ◽  
Sebaceous Gland ◽  
Rats And Mice ◽  
Mixed Tumors

Sebaceous gland tumors and other tumors exhibiting sebaceous differentiation have been described in humans (1,2,3). Tumors of the sebaceous gland can be induced in rats and mice following topical application of carcinogens (4), but spontaneous mixed tumors of basal cell origin rarely occur in mice.

Basal cell carcinoma of the nail bed in a Korean woman

2000 ◽  
Vol 39 (5) ◽  
pp. 397-398 ◽  
Author(s):  
Hyoung-Joo Kim ◽  
Youn-Soo Kim ◽  
Ki-Beom Suhr ◽  
Tae-Young Yoon ◽  
Jeung-Hoon Lee ◽  
...  
Keyword(s):  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Korean Woman ◽  
Nail Bed

Basal cell carcinoma of the nipple

1978 ◽  
Vol 114 (12) ◽  
pp. 1845-1845 ◽  
Author(s):  
G. P. Lupton
Keyword(s):  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell
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