Oncology/Hematology

Deep Learning-Based TEM Image Analysis for Fully Automated Detection of Gold Nanoparticles Internalized Within Tumor Cell.

Transmission electron microscopy (TEM) imaging can be used for detection/localization of gold nanoparticles (GNPs) within tumor cells. However, quantitative analysis of GNP-containing cellular TEM images typically relies on conventional/thresholding-based methods, which are manual, time-consuming, and prone to human errors. In this study, therefore, deep learning (DL)-based methods were developed ...

ProgCAE: a deep learning-based method that integrates multi-omics data to predict cancer subtypes.

Determining cancer subtypes and estimating patient prognosis are crucial for cancer research. The massive amount of multi-omics data generated by high-throughput sequencing technology is an important resource for cancer prognosis. Deep learning methods can integrate such data to accurately identify more cancer subtypes. We propose a prognostic model based on a convolutional autoencoder (ProgCAE) t...

MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction.

Classifying epitopes is essential since they can be applied in various fields, including therapeutics, diagnostics and peptide-based vaccines. To dete...

Predicting potential microbe-disease associations based on multi-source features and deep learning.

Studies have confirmed that the occurrence of many complex diseases in the human body is closely related to the microbial community, and microbes can ...

Development of a deep learning-based error detection system without error dose maps in the patient-specific quality assurance of volumetric modulated arc therapy.

To detect errors in patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), we proposed an error detection method based o...

Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction.

Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstru...

DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations.

Driver mutations can contribute to the initial processes of cancer, and their identification is crucial for understanding tumorigenesis as well as for...

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.

The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form ...

Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis.

INTRODUCTION: Differentiation of histologically similar structures in the liver, including anatomical structures, benign bile duct lesions, or common ...

[Experience in performing robot-assisted radical prostatectomy in patients after heart transplan- tation].

INTRODUCTION: Prostate cancer (PCa) occupies a leading position in the structure of oncological morbidity and mortality and is an urgent problem of mo...

Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia.

PURPOSE: Matching patients to clinical trials is cumbersome and costly. Attempts have been made to automate the matching process; however, most have u...

[Robot-assisted Thoracoscopic Surgery for Mediastinal Tumor in Our Department].

In 2019, our facility introduced robot-assisted surgery for mediastinal tumors using da Vinci Si, which was upgrade to da Vinci X and Xi in 2021. Init...

[Minimally Invasive Robot-assisted Thoracoscopic Surgery for Mediastinal Tumor].

Robot-assisted thoracoscopic surgery( RATS) and video-assisted thoracoscopic surgery are minimally invasive surgical approaches to the chest wall that...

[Robot-assisted Thoracic Surgery for Mediastinal and Chest Wall Tumors:Atypical Surgical Approaches by the Tumor Localization].

Surgery for mediastinal and chest wall tumors requires various approaches, including open and thoracoscopic, depending on the size and localization of...

A Deep Learning Framework for Skull Stripping in Brain MRI.

Skull-stripping, an important pre-processing step in neuroimage computing, involves the automated removal of non-brain anatomy (such as the skull, eye...

Mining Whole-liver Information with Deep Learning for Preoperatively Predicting HCC Recurrence-free Survival.

Hepatocellular carcinoma (HCC) is globally a leading cause of cancer death. Non-invasive pre-operative prediction of HCC recurrence-free survival (RFS...

A deep learning model based on the combination of convolutional and recurrent neural networks to enhance pulse oximetry ability to classify sleep stages in children with sleep apnea.

Characterization of sleep stages is essential in the diagnosis of sleep-related disorders but relies on manual scoring of overnight polysomnography (P...

Immune Activation Modulation via Magnetically Localized Bacteria Based Micro/Bio Robot (BBMBR).

Understanding tumor's microenvironment is one of the key factors in the cancer therapy. Especially, from the perspective of immunotherapy, immune dese...

Multi-class Tissue Segmentation of CT images using an Ensemble Deep Learning method.

Microwave ablation (MWA) therapy is a well-known technique for locally destroying lung tumors with the help of computed tomography (CT) images. Howeve...

Spatiotemporal Mammography-based Deep Learning Model for Improved Breast Cancer Risk Prediction.

Breast cancer is one of the most prevalent cancers among women. It is the second leading cause of death in cancer-related deaths. Early detection and ...