Oncology/Hematology

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation.

Brain tumor has the foremost distinguished etiology of high morality. Neoplasm, a categorization of brain tumors, is very operative in distinguishing and determining the tumor's exact location in the brain. Magnetic resonance imaging (MRI) is an efficient noninvasive technique for the anatomical examination of brain tumors. Growth tissues have a distinguishable look in MRI pictures in order that t...

Breast cancer patient characterisation and visualisation using deep learning and fisher information networks.

Breast cancer is the most commonly diagnosed female malignancy globally, with better survival rates if diagnosed early. Mammography is the gold standard in screening programmes for breast cancer, but despite technological advances, high error rates are still reported. Machine learning techniques, and in particular deep learning (DL), have been successfully used for breast cancer detection and clas...

Deep learning can predict survival directly from histology in clear cell renal cell carcinoma.

For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artif...

Attention Mask R-CNN with edge refinement algorithm for identifying circulating genetically abnormal cells.

Recent studies have suggested that circulating tumor cells with abnormalities in gene copy numbers in mononuclear cell-enriched peripheral blood sampl...

Fusion of CT images and clinical variables based on deep learning for predicting invasiveness risk of stage I lung adenocarcinoma.

PURPOSE: To develop a novel multimodal data fusion model by incorporating computed tomography (CT) images and clinical variables based on deep learnin...

Biomarker identification by reversing the learning mechanism of an autoencoder and recursive feature elimination.

RNA-Seq has made significant contributions to various fields, particularly in cancer research. Recent studies on differential gene expression analysis...

Nuclear morphology is a deep learning biomarker of cellular senescence.

Cellular senescence is an important factor in aging and many age-related diseases, but understanding its role in health is challenging due to the lack...

Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis.

Machine learning (ML) algorithms are becoming increasingly pervasive in the domains of medical diagnostics and prognostication, afforded by complex de...

Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images.

Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in ...

Deep Learning-Based CT Imaging in the Diagnosis of Treatment Effect of Pulmonary Nodules and Radiofrequency Ablation.

To study the effect of computerized tomography (CT) images based on deep learning algorithms on the diagnosis of pulmonary nodules and the effect of r...

Identification of Human Cell Cycle Phase Markers Based on Single-Cell RNA-Seq Data by Using Machine Learning Methods.

The cell cycle is composed of a series of ordered, highly regulated processes through which a cell grows and duplicates its genome and eventually divi...

Development of deep learning chest X-ray model for cardiac dose prediction in left-sided breast cancer radiotherapy.

Deep inspiration breath-hold (DIBH) is widely used to reduce the cardiac dose in left-sided breast cancer radiotherapy. This study aimed to develop a ...

Deep learning-based diagnosis from endobronchial ultrasonography images of pulmonary lesions.

Endobronchial ultrasonography with a guide sheath (EBUS-GS) improves the accuracy of bronchoscopy. The possibility of differentiating benign from mali...

Towards Precision Oncology: Enhancing Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence.

Highly complex and multi-dimensional medical data containing clinical, radiologic, pathologic, and sociodemographic information have the potential to ...

Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM.

Clinical diagnosis has become very significant in today's health system. The most serious disease and the leading cause of mortality globally is brai...

Anti-Cancer Drug Solubility Development within a Green Solvent: Design of Novel and Robust Mathematical Models Based on Artificial Intelligence.

Nowadays, supercritical CO(SC-CO) is known as a promising alternative for challengeable organic solvents in the pharmaceutical industry. The mathemati...

Why pay more for robot in esophageal cancer surgery?

Esophagectomy is the gold standard for the treatment of resectable esophageal cancer. Traditionally, it is performed through a laparotomy and a thorac...

Machine learning applications in upper gastrointestinal cancer surgery: a systematic review.

BACKGROUND: Machine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper...

One-step Endovascular Aortic Aneurysm Repair With CO2 contrast and Robotic Partial Nephrectomy for Renal Cell Carcinoma: Case Report.

INTRODUCTION: Although rare, as the population ages, abdominal aortic aneurysm synchronous to abdominal malignancies, as renal cell carcinoma, is expe...

A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: a development and validation study.

BACKGROUND: The DoMore-v1-CRC marker was recently developed using deep learning and conventional haematoxylin and eosin-stained tissue sections, and w...