Oncology/Hematology

A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers.

Brain tumors are difficult to treat and cause substantial fatalities worldwide. Medical professionals visually analyze the images and mark out the tumor regions to identify brain tumors, which is time-consuming and prone to error. Researchers have proposed automated methods in recent years to detect brain tumors early. These approaches, however, encounter difficulties due to their low accuracy and...

Models of Artificial Intelligence-Assisted Diagnosis of Lung Cancer Pathology Based on Deep Learning Algorithms.

In this article, in order to explore the application of a diagnosis system for lung cancer, we use an auxiliary diagnostic system to predict and diagnose the good and evil attributes of chest CT pulmonary nodules. This research improves the new diagnosis method based on the convolutional neural network (CNN) and the recurrent neural network (RNN) and combines the dual effects of the two algorithms...

Cancer insights from magnetic resonance spectroscopy of cells and excised tumors.

Multinuclear ex vivo magnetic resonance spectroscopy (MRS) of cancer cells, xenografts, human cancer tissue, and biofluids is a rapidly expanding fiel...

Regression trees and ensembles for cumulative incidence functions.

The use of cumulative incidence functions for characterizing the risk of one type of event in the presence of others has become increasingly popular o...

Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning.

Tumor grade assessment is critical to the treatment of cancers. A pathologist typically evaluates grade by examining morphologic organization in tissu...

The robotic learning curve for a newly appointed colorectal surgeon.

Robotic colorectal surgery allows for better ergonomics, superior retraction, and fine movements in the narrow anatomy of the pelvis. Recent years hav...

Application of Deep Learning in Breast Cancer Imaging.

This review gives an overview of the current state of deep learning research in breast cancer imaging. Breast imaging plays a major role in detecting ...

A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer.

PURPOSE: Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chem...

Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning.

Skin cancer is a major type of cancer with rapidly increasing victims all over the world. It is very much important to detect skin cancer in the early...

Concurrent robot-assisted radical prostatectomy and robot-assisted partial nephrectomy for patients with synchronous prostate cancer and small renal tumor: A case series of five patients.

Robotic surgery has become widely used in the field of urology. We experienced concurrent robot-assisted radical prostatectomy (RARP) and robot-assist...

Medical image diagnosis of prostate tumor based on PSP-Net+VGG16 deep learning network.

BACKGROUND AND OBJECTIVE: Prostate cancer is the most common cancer of the male reproductive system. With the development of medical imaging technolog...

Glycotherapy: A New Paradigm in Breast Cancer Research.

Breast cancer is an ancient disease recognized first by the Egyptians as early as 1600 BC. The first cancer-causing gene in a chicken tumor virus was ...

Three-Dimensional Ultrasound Images in the Assessment of Bladder Tumor Health Monitoring under Deep Learning Algorithms.

This study was aimed at exploring the application value of three-dimensional (3D) ultrasound based on deep learning and continued nursing health monit...

Diagnosis of Early Cervical Cancer with a Multimodal Magnetic Resonance Image under the Artificial Intelligence Algorithm.

This research was conducted to explore the value of multimodal magnetic resonance imaging (MRI) based on the alternating direction algorithm in the di...

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy.

Artificial intelligence (AI) is widely used to analyze gastrointestinal (GI) endoscopy image data. AI has led to several clinically approved algorithm...

Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network.

Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagno...

Deep Learning-Based Classification for Melanoma Detection Using XceptionNet.

Skin cancer is one of the most common types of cancer in the world, accounting for at least 40% of all cancers. Melanoma is considered as the 19th mos...

Robotic biliary surgery for benign and malignant bile duct obstruction: a case series.

The majority of patients with benign or malignant biliary obstruction require surgical treatment with a bilio-enteric anastomosis. This requires fine ...

Roughness of the renal tumor surface could predict the surgical difficulty of robot-assisted partial nephrectomy.

INTRODUCTION: Preoperative prediction of surgical difficulty of partial nephrectomy (PN) is essential to minimize the perioperative complications and ...

Starting CT-guided robotic interventional oncology at a UK centre.

OBJECTIVE: A commercially available CT-guided robot offers enhanced abilities in planning, targeting, and confirming accurate needle placement. In thi...