AIMC Topic: Carcinoma, Squamous Cell

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Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks.

Scientific reports Feb 24, 2022
In radiation oncology, predicting patient risk stratification allows specialization of therapy intensification as well as selecting between systemic and regional treatments, all of which helps to improve patient outcome and quality of life. Deep lear...
Prognosis Retrospective Studies Female Carcinoma, Squamous Cell Humans Neural Networks, Computer Head and Neck Neoplasms Positron Emission Tomography Computed Tomography Diagnosis, Computer-Assisted Neoplasm Recurrence, Local Aged Deep Learning Attention Biomarkers, Tumor Image Processing, Computer-Assisted Aged, 80 and over Male Adult Middle Aged Quality of Life
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Exploring the Study of miR-1301 Inhibiting the Proliferation and Migration of Squamous Cell Carcinoma YD-38 Cells through PI3K/AKT Pathway under Deep Learning Medical Images.

Computational intelligence and neuroscience Feb 10, 2022
With the rapid development and application of deep learning medical image recognition, natural language processing, and other fields, at the same time, deep learning has become the most popular research direction in the field of image processing and ...
Phosphatidylinositol 3-Kinases Proto-Oncogene Proteins c-akt Humans Carcinoma, Squamous Cell Deep Learning Cell Proliferation Signal Transduction Cell Movement MicroRNAs
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Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch.

Scientific reports Feb 3, 2022
Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackl...
Lung Lung Neoplasms Small Cell Lung Carcinoma Carcinoma, Non-Small-Cell Lung Humans Area Under Curve Biopsy Adenocarcinoma of Lung Neural Networks, Computer Image Interpretation, Computer-Assisted Eosine Yellowish-(YS) Carcinoma, Squamous Cell Datasets as Topic Hematoxylin Histocytochemistry
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BID-Net: An Automated System for Bone Invasion Detection Occurring at Stage T4 in Oral Squamous Carcinoma Using Deep Learning.

Computational intelligence and neuroscience Jan 30, 2022
Detection of the presence and absence of bone invasion by the tumor in oral squamous cell carcinoma (OSCC) patients is very significant for their treatment planning and surgical resection. For bone invasion detection, CT scan imaging is the preferred...
Tomography, X-Ray Computed Carcinoma, Squamous Cell Deep Learning Radiologists Humans Mouth Neoplasms
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Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images.

Scientific reports Dec 13, 2021
Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry labelling to confirm the...
Software Carcinoma, Squamous Cell Sensitivity and Specificity Carcinoma, Non-Small-Cell Lung Image Interpretation, Computer-Assisted Immunohistochemistry Humans Machine Learning
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Deep learning data augmentation for Raman spectroscopy cancer tissue classification.

Scientific reports Dec 13, 2021
Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design...
Carcinoma, Squamous Cell Diagnosis, Computer-Assisted Deep Learning Spectrum Analysis, Raman Melanoma Skin Neoplasms Carcinoma, Basal Cell Humans
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Learning-based synthetic dual energy CT imaging from single energy CT for stopping power ratio calculation in proton radiation therapy.

The British journal of radiology Oct 28, 2021
OBJECTIVE: Dual energy CT (DECT) has been shown to estimate stopping power ratio (SPR) map with a higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interactions. This work presents a learning-based...
Radiotherapy Planning, Computer-Assisted Proton Therapy Retrospective Studies Tomography, X-Ray Computed Humans Carcinoma, Squamous Cell Machine Learning Head and Neck Neoplasms
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Weakly supervised learning for classification of lung cytological images using attention-based multiple instance learning.

Scientific reports Oct 13, 2021
In cytological examination, suspicious cells are evaluated regarding malignancy and cancer type. To assist this, we previously proposed an automated method based on supervised learning that classifies cells in lung cytological images as benign or mal...
Lung Deep Learning Image Processing, Computer-Assisted Lung Neoplasms Adenocarcinoma Supervised Machine Learning Image Interpretation, Computer-Assisted Neural Networks, Computer Retrospective Studies Carcinoma, Squamous Cell Reproducibility of Results Humans Small Cell Lung Carcinoma Chromatin Medical Informatics Pattern Recognition, Automated Thorax
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Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Sep 14, 2021
Intraoperative detection of the marginal tissues is the last and most important step to complete the resection of adenocarcinoma and squamous cell carcinoma. However, the current intraoperative diagnosis is time-consuming and requires numerous steps ...
Lung Neoplasms Humans Spectrum Analysis, Raman Carcinoma, Squamous Cell Adenocarcinoma Deep Learning Adenocarcinoma of Lung
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Selection, Visualization, and Interpretation of Deep Features in Lung Adenocarcinoma and Squamous Cell Carcinoma.

The American journal of pathology Sep 8, 2021
Although deep learning networks applied to digital images have shown impressive results for many pathology-related tasks, their black-box approach and limitation in terms of interpretability are significant obstacles for their widespread clinical uti...
Diagnosis, Differential Humans Feasibility Studies Neural Networks, Computer Image Interpretation, Computer-Assisted Female Carcinoma, Squamous Cell Datasets as Topic Reproducibility of Results Sensitivity and Specificity Deep Learning Adenocarcinoma of Lung Male Image Processing, Computer-Assisted Lung Neoplasms
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