Results 21 to 30 of about 217,264 (271)

GDN: A Stacking Network Used for Skin Cancer Diagnosis [PDF]

open access: yes, 2023
Skin cancer, the primary type of cancer that can be identified by visual recognition, requires an automatic identification system that can accurately classify different types of lesions. This paper presents GoogLe-Dense Network (GDN), which is an image-classification model to identify two types of skin cancer, Basal Cell Carcinoma, and Melanoma.
arxiv   +1 more source

Nodular basal cell carcinoma [PDF]

open access: yesPraxis Medica, 2016
Basal cell carcinoma is a slow-growing malignant skin tumor and its often found in elderly patients on sun exposed areas that confirming chronic UV radiation.
Vukanović Tatjana   +4 more
doaj   +1 more source

Programmed death ligand 1 expression levels, clinicopathologic features, and survival in surgically resected sarcomatoid lung carcinoma [PDF]

open access: yesAsia Pac J Clin Oncol 2020 1 9, 2020
Aim: To determine the programmed death ligand-1 (PD-L1) expression rates in sarcomatoid lung carcinomas and to compare clinicopathologic features and survival rates of PD-L1-positive and negative patients. Methods: PD-L1 expression was evaluated in 65 surgically resected sarcomatoid carcinomas.
arxiv   +1 more source

Expression patterns of α2,3-Sialyltransferase I and α2,6-Sialyltransferase I in human cutaneous epithelial lesions

open access: yesEuropean Journal of Histochemistry, 2013
Skin tumors have become one of the most common cancers in the world and their carcinogenesis is frequently associated with altered glycosylation patterns. The aberrant sialylation, a type of glycosylation, can mediate pathophysiological key events during
S.A. Ferreira   +6 more
doaj   +1 more source

Extremely Rare Pulmonary Metastases of Skin Basal Cell Carcinoma: Report of Two Cases with Clinicopathological Features

open access: yesBiomedicines, 2023
Basal cell carcinoma (BCC) is the most frequent human skin cancer, but metastasizing BCC (MBCC) is extremely rare, developing in approximately 0.0028% to 0.55% of BCC patients. Herein, we report two cases of pulmonary MBCC.
Małgorzata Edyta Wojtyś   +8 more
doaj   +1 more source

EARLY DIAGNOSTIC ELEMENTS IN FACIAL BASAL CELL CARCINOMA

open access: yesRomanian Journal of Medical and Dental Education, 2022
Basal cell carcinoma (BCC) arises and evolves through intricately mixed, intertwined actions between phenotypic, genetic and environmental factors. Despite being the preponderant type of neoplasia and its frequent location in the accessible and visible ...
Anca Irina Grădinariu   +6 more
doaj  

Update on Hedgehog Pathway Inhibitor Therapy for Patients with Basal Cell Naevus Syndrome or High-frequency Basal Cell Carcinoma

open access: yesActa Dermato-Venereologica, 2022
Some patients with basal cell carcinoma develop a large number of basal cell carcinomas during their lives. The most common underlying genetic disease that causes multiple basal cell carcinomas is basal cell naevus syndrome. Basal cell naevus syndrome is
Babette J.A. Verkouteren   +4 more
doaj   +1 more source

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms [PDF]

open access: yesNature Scientific Reports 17912 (2017), 2017
Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment.
arxiv   +1 more source

Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of Basal Cell Carcinoma (BCC) [PDF]

open access: yesarXiv, 2021
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 pathologists by marking critical regions that have a high probability of exhibiting pathological features in Whole Slide Images (WSI).
arxiv  

Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma [PDF]

open access: yes, 2020
This chapter presents a methodology for diagnosis of pigmented skin lesions using convolutional neural networks. The architecture is based on convolu-tional neural networks and it is evaluated using new CNN models as well as re-trained modification of pre-existing CNN models were used. The experi-mental results showed that CNN models pre-trained on big
arxiv   +1 more source

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