Other Cancers

Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression.

Nowadays, the most frequent cancer in women is breast cancer (malignant tumor). If breast cancer is ...

CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach.

PURPOSE: This study aimed to investigate whether a machine learning-based computed tomography (CT) t...

Lung Cancer Detection using Probabilistic Neural Network with modified Crow-Search Algorithm.

Objective: Lung cancer is a type of malignancy that occurs most commonly among men and the third mos...

Brain tumor classification using deep CNN features via transfer learning.

Brain tumor classification is an important problem in computer-aided diagnosis (CAD) for medical app...

Robotic technology for palliative and supportive care: Strengths, weaknesses, opportunities and threats.

BACKGROUND: Medical robots are increasingly used for a variety of applications in healthcare. Robots...

Improved Deep Learning Network Based in combination with Cost-sensitive Learning for Early Detection of Ovarian Cancer in Color Ultrasound Detecting System.

With the development of theories and technologies in medical imaging, most of the tumors can be dete...

Handcrafted versus deep learning radiomics for prediction of cancer therapy response.

TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set.

We propose a new deep learning approach for medical imaging that copes with the problem of a small t...

Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network.

PURPOSE: Colorectal tumor segmentation is an important step in the analysis and diagnosis of colorec...

MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer.

PURPOSE: To develop and validate an Artificial Intelligence (AI) model based on texture analysis of ...

Prediction of early colorectal cancer metastasis by machine learning using digital slide images.

BACKGROUND AND OBJECTIVES: Prediction of lymph node metastasis (LNM) for early colorectal cancer (CR...

Automatic gas detection in prostate cancer patients during image-guided radiation therapy using a deep convolutional neural network.

PURPOSE: The detection of intestinal/rectal gas is very important during image-guided radiation ther...

Artificial neural network models to predict nodal status in clinically node-negative breast cancer.

BACKGROUND: Sentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in brea...

Classification of benign and malignant lung nodules from CT images based on hybrid features.

The classification of benign and malignant lung nodules has great significance for the early detecti...

Presurgical differentiation between malignant haemangiopericytoma and angiomatous meningioma by a radiomics approach based on texture analysis.

PURPOSE: To assess whether a machine-learning model based on texture analysis (TA) could yield a mor...

Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms.

Background Computational models on the basis of deep neural networks are increasingly used to analyz...

CT evaluation of extranodal extension of cervical lymph node metastases in patients with oral squamous cell carcinoma using deep learning classification.

OBJECTIVE: To clarify CT diagnostic performance in extranodal extension of cervical lymph node metas...

Automatic CIN Grades Prediction of Sequential Cervigram Image Using LSTM With Multistate CNN Features.

Cervical cancer ranks as the second most common cancer in women worldwide. In clinical practice, col...

A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

PURPOSE: Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can ...

Markerless Pancreatic Tumor Target Localization Enabled By Deep Learning.

PURPOSE: Deep learning is an emerging technique that allows us to capture imaging information beyond...

Comparison of F-FDG avidity at PET of benign and malignant pure ground-glass opacities: a paradox? Part II: artificial neural network integration of the PET/CT characteristics of ground-glass opacities to predict their likelihood of malignancy.

AIM: To assess the ability of artificial neural networks (ANNs) to predict the likelihood of maligna...