Oncology/Hematology

Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning.

OBJECTIVES: To evaluate the performance of deep learning using ResNet50 in differentiation of benign and malignant vertebral fracture on CT.

Esophageal cancer detection based on classification of gastrointestinal CT images using improved Faster RCNN.

PURPOSE: Esophageal cancer is a common malignant tumor in life, which seriously affects human health. In order to reduce the work intensity of doctors and improve detection accuracy, we proposed esophageal cancer detection using deep learning. The characteristics of deep learning: association and structure, activity and experience, essence and variation, migration and application, value and evalua...

Dual energy CT image prediction on primary tumor of lung cancer for nodal metastasis using deep learning.

Lymph node metastasis (LNM) identification is the most clinically important tasks related to survival and recurrence from lung cancer. However, the pr...

Role of deep learning in brain tumor detection and classification (2015 to 2020): A review.

During the last decade, computer vision and machine learning have revolutionized the world in every way possible. Deep Learning is a sub field of mach...

Deep-learning-based image registration and automatic segmentation of organs-at-risk in cone-beam CT scans from high-dose radiation treatment of pancreatic cancer.

PURPOSE: Accurate deformable registration between computed tomography (CT) and cone-beam CT (CBCT) images of pancreatic cancer patients treated with h...

Knowledge-infused Global-Local Data Fusion for Spatial Predictive Modeling in Precision Medicine.

The automated capability of generating spatial prediction for a variable of interest is desirable in various science and engineering domains. Take Pre...

Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence.

BACKGROUND: The presence of nodal metastases is important in the treatment of papillary thyroid carcinoma (PTC). We present our experience using a con...

A CNN-based unified framework utilizing projection loss in unison with label noise handling for multiple Myeloma cancer diagnosis.

Multiple Myeloma (MM) is a malignancy of plasma cells. Similar to other forms of cancer, it demands prompt diagnosis for reducing the risk of mortalit...

Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.

Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance im...

A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis.

Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limi...

Learning deep features for dead and living breast cancer cell classification without staining.

Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most com...

A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens.

BACKGROUND: Although deep learning algorithms for clinical cytology have recently been developed, their application to practical assistance systems ha...

Deep learning for predicting COVID-19 malignant progression.

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged publ...

CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training.

BACKGROUND: The state-of-the-art deep learning based cancer type prediction can only predict cancer types whose samples are available during the train...

Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction.

PURPOSE: To evaluate image quality, image noise, and conspicuity of pancreatic ductal adenocarcinoma (PDAC) in pancreatic low-dose computed tomography...

Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review.

Advances in artificial intelligence-based methods have led to the development and publication of numerous systems for auto-segmentation in radiotherap...

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.

Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitiv...

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for ...

A Visually Interpretable Deep Learning Framework for Histopathological Image-Based Skin Cancer Diagnosis.

Owing to the high incidence rate and the severe impact of skin cancer, the precise diagnosis of malignant skin tumors is a significant goal, especiall...

Application of artificial intelligence in gynecologic malignancies: A review.

With the development of machine learning and deep learning models, artificial intelligence is now being applied to the field of medicine. In oncology,...