Oncology/Hematology

BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network.

The public health system is extremely dependent on the use of vaccines to immunize the population from a series of infectious and dangerous diseases, preventing the system from collapsing and millions of people dying every year. However, to develop these vaccines and effectively monitor these diseases, it is necessary to use accurate diagnostic methods capable of identifying highly immunogenic reg...

A deep learning approach to detect blood vessels in basal cell carcinoma.

PURPOSE: Blood vessels called telangiectasia are visible in skin lesions with the aid of dermoscopy. Telangiectasia are a pivotal identifying feature of basal cell carcinoma. These vessels appear thready, serpiginous, and may also appear arborizing, that is, wide vessels branch into successively thinner vessels. Due to these intricacies, their detection is not an easy task, neither with manual ann...

Prediction of the position of external markers using a recurrent neural network trained with unbiased online recurrent optimization for safe lung cancer radiotherapy.

BACKGROUND AND OBJECTIVE: During lung cancer radiotherapy, the position of infrared reflective objects on the chest can be recorded to estimate the tu...

Deep learning with whole slide images can improve the prognostic risk stratification with stage III colorectal cancer.

BACKGROUND AND OBJECTIVE: Adjuvant chemotherapy is recommended as standard treatment for colorectal cancer (CRC) with stage III according to TNM stage...

Magnetic Resonance Imaging Features on Deep Learning Algorithm for the Diagnosis of Nasopharyngeal Carcinoma.

The objective of this research was to investigate the application values of magnetic resonance imaging (MRI) features of the deep learning-based image...

Diagnosis of Nonperitonealized Colorectal Cancer with Computerized Tomography Image Features under Deep Learning.

This study aimed to explore the value of abdominal computerized tomography (CT) three-dimensional reconstruction using the dense residual single-axis ...

Robotic nephroureterectomy in the management of upper tract urothelial cancer: inching toward standard of care?

Upper-tract urothelial carcinoma is a relatively rare malignancy. Current guidelines strongly recommend radical nephroureterectomy with bladder cuff e...

Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels.

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance w...

Computed Tomography Texture Features and Risk Factor Analysis of Postoperative Recurrence of Patients with Advanced Gastric Cancer after Radical Treatment under Artificial Intelligence Algorithm.

Computer tomography texture analysis (CTTA) based on the V-Net convolutional neural network (CNN) algorithm was used to analyze the recurrence of adva...

Machine Learning Models for the Diagnosis and Prognosis Prediction of High-Grade B-Cell Lymphoma.

High-grade B-cell lymphoma (HGBL) is a newly introduced category of rare and heterogeneous invasive B-cell lymphoma (BCL), which is diagnosed dependin...

A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal squamous cell carcinoma based on endoscopic images using deep learning.

OBJECTIVES: To propose deep-learning (DL)-based predictive model for pathological complete response rate for resectable locally advanced esophageal sq...

Patient Benefit and Quality of Life after Robot-Assisted Head and Neck Surgery.

Robotic systems for head and neck surgery are at different stages of technical development and clinical application. Currently, robotic systems are pr...

Computer-aided detection and segmentation of malignant melanoma lesions on whole-body F-FDG PET/CT using an interpretable deep learning approach.

BACKGROUND AND OBJECTIVE: In oncology, 18-fluorodeoxyglucose (F-FDG) positron emission tomography (PET) / computed tomography (CT) is widely used to i...

Design of Nuclear Radiation Monitoring System in Floor Exploration Based on Deep Learning.

Nuclear radiation environmental monitoring has become an important issue in floor surveys. From the perspective of regional environmental nuclear radi...

AI-based optimization for US-guided radiation therapy of the prostate.

OBJECTIVES: Fast volumetric ultrasound presents an interesting modality for continuous and real-time intra-fractional target tracking in radiation the...

Deep learning-based multimodal image analysis for cervical cancer detection.

Cervical cancer is the fourth most common cancer in women, and its precise detection plays a critical role in disease treatment and prognosis predicti...

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.

High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dim...

Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images.

In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object re...

A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer.

BACKGROUND: Recent studies showed the potential of MRI-based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI...

A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology.

Epstein-Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessi...