Oncology/Hematology

Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation.

Due to the black box model nature of convolutional neural networks, computer-aided diagnosis methods based on depth learning are usually poorly interpretable. Therefore, the diagnosis results obtained by these unexplained methods are difficult to gain the trust of patients and doctors, which limits their application in the medical field. To solve this problem, an interpretable depth learning image...

Application of Artificial Intelligence Nuclear Medicine Automated Images Based on Deep Learning in Tumor Diagnosis.

In order to correctly obtain normal tissues and organs and tumor lesions, the research on multimodal medical image segmentation based on deep learning fully automatic segmentation algorithm is more meaningful. This article aims to study the application of deep learning-based artificial intelligence nuclear medicine automated images in tumor diagnosis. This paper studies the methods to improve the ...

BID-Net: An Automated System for Bone Invasion Detection Occurring at Stage T4 in Oral Squamous Carcinoma Using Deep Learning.

Detection of the presence and absence of bone invasion by the tumor in oral squamous cell carcinoma (OSCC) patients is very significant for their trea...

Low-radiation dose scan protocol for preoperative imaging for dental implant surgery using deep learning-based reconstruction in multidetector CT.

OBJECTIVES: This study aimed to investigate the impact of a deep learning-based reconstruction (DLR) technique on image quality and reduction of radia...

Using deep learning models to analyze the cerebral edema complication caused by radiotherapy in patients with intracranial tumor.

Using deep learning models to analyze patients with intracranial tumors, to study the image segmentation and standard results by clinical depiction co...

Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer.

Globally with over 10 million deaths per year, cancer is the most transversal disease across countries, cultures, and ethnicities, affecting both deve...

Development of a deep learning-based patient-specific target contour prediction model for markerless tumor positioning.

PURPOSE: For pancreatic cancer patients, image guided radiation therapy and real-time tumor tracking (RTTT) techniques can deliver radiation to the ta...

Disease-Specific Imaging Utilizing Support Vector Machine Classification of H-Scan Parameters: Assessment of Steatosis in a Rat Model.

In medical imaging, quantitative measurements have shown promise in identifying diseases by classifying normal versus pathological parameters from tis...

WBC-AMNet: Automatic classification of WBC images using deep feature fusion network based on focalized attention mechanism.

The recognition and classification of White Blood Cell (WBC) play a remarkable role in blood-related diseases (i.e., leukemia, infections) diagnosis. ...

Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach.

Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally. Breast cancer detection needs accurate mammography inte...

A deep learning model for breast ductal carcinoma in situ classification in whole slide images.

The pathological differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of pivotal importance fo...

Extent of pelvic lymph node dissection improves early oncological outcomes for patients with high-risk prostate cancer without lymph node involvement after robot-assisted radical prostatectomy.

BACKGROUND: We investigated the effect of the extent of pelvic lymph node dissection (LND) on biochemical recurrence (BCR) in patients with prostate c...

DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer's Disease.

Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantia...

Palliative Care Exposure Relative to Predicted Risk of Six-Month Mortality in Hospitalized Adults.

CONTEXT: The optimal strategy for implementing mortality-predicting algorithms to facilitate clinical care, prognostic discussions, and palliative car...

Deep-learning 2.5-dimensional single-shot detector improves the performance of automated detection of brain metastases on contrast-enhanced CT.

PURPOSE: This study aims to develop a 2.5-dimensional (2.5D) deep-learning, object detection model for the automated detection of brain metastases, in...

Deep learning image reconstruction algorithm for abdominal multidetector CT at different tube voltages: assessment of image quality and radiation dose in a phantom study.

OBJECTIVES: To compare the image quality and radiation dose of a deep learning image reconstruction (DLIR) algorithm compared with iterative reconstru...

High-strength deep learning image reconstruction in coronary CT angiography at 70-kVp tube voltage significantly improves image quality and reduces both radiation and contrast doses.

OBJECTIVES: To explore the use of 70-kVp tube voltage combined with high-strength deep learning image reconstruction (DLIR-H) in reducing radiation an...

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion.

After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be re...

Multi-channel convolutional neural network architectures for thyroid cancer detection.

Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid speci...

Robot-assisted versus video-assisted thoraco-laparoscopic McKeown's esophagectomy for esophageal cancer: a propensity score-matched analysis of minimally invasive approaches.

Minimally invasive esophagectomy for esophageal cancer decreases overall complication rate and leads to faster postoperative recovery. Robot-assisted ...