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Carcinoma, Squamous Cell

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Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists.

Scientific reports
The study aims to identify histological classifiers from histopathological images of oral squamous cell carcinoma using convolutional neural network (CNN) deep learning models and shows how the results can improve diagnosis. Histopathological samples...

Effect of radiotherapy on phagocytosis percentage and index in patients with oral squamous cell carcinoma.

Journal of cancer research and therapeutics
BACKGROUND: Phagocytosis plays an important role in the fundamental process of immunity and maintains systemic tissue homeostasis. Phagocytosis function is assessed in radiotherapy to signify the prognosis of patient. Therefore, we designed a study t...

Early screening of cervical cancer based on tissue Raman spectroscopy combined with deep learning algorithms.

Photodiagnosis and photodynamic therapy
Cervical cancer is the most common reproductive malignancy in the female reproductive system. The incidence rate and mortality rate of cervical cancer among women in China are high. In this study, Raman spectroscopy was used to collect tissue sample ...

Deep learning-based semantic segmentation of non-melanocytic skin tumors in whole-slide histopathological images.

Experimental dermatology
Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the two most common skin cancer and impose a huge medical burden on society. Histopathological examination based on whole-slide images (WSIs) remains to be the confirmatory diagnostic m...

Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions.

Cancer medicine
BACKGROUND: Colposcopy is indispensable for the diagnosis of cervical lesions. However, its diagnosis accuracy for high-grade squamous intraepithelial lesion (HSIL) is at about 50%, and the accuracy is largely dependent on the skill and experience of...

An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images.

EBioMedicine
BACKGROUND: Determining the origin of bone metastatic cancer (OBMC) is of great significance to clinical therapeutics. It is challenging for pathologists to determine the OBMC with limited clinical information and bone biopsy.

A one-dimensional convolutional neural network based deep learning for high accuracy classification of transformation stages in esophageal squamous cell carcinoma tissue using micro-FTIR.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Among the most frequently diagnosed cancers in developing countries, esophageal squamous cell carcinoma (ESCC) ranks among the top six causes of death. It would be beneficial if a rapid, accurate, and automatic ESCC diagnostic method could be develop...

Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer.

Medical image analysis
Although deep learning (DL) has demonstrated impressive diagnostic performance for a variety of computational pathology tasks, this performance often markedly deteriorates on whole slide images (WSI) generated at external test sites. This phenomenon ...

Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology.

Nature communications
A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, ...