Deep Learning Articles

This study aims to investigate the feasibility of using convolutional neural networks to predict an accurate and high resolution dose distribution from an approximated and low resolution input dose.

Sperm morphology analysis (SMA) is a very important factor in the diagnosis process of male infertility. This research proposes a novel deep learning algorithm for malformation detection of sperm morphology using human sperm cell images.

Optimal detection and surveillance of bladder cancer (BCa) rely primarily on the cystoscopic visualization of bladder lesions. AI-assisted cystoscopy may improve image recognition and accelerate data acquisition.

Adequate tumor detection is critical in complete transurethral resection of bladder tumor (TURBT) to reduce cancer recurrence, but up to 20% of bladder tumors are missed by standard white light cystoscopy.

We propose a deep learning-based image interpretation system for skeleton segmentation and extraction of hot spots of bone metastatic lesion from a whole-body bone scintigram followed by automated measurement of a bone scan index (BSI), which will be clinically useful.

Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for MRI-based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network (GAN)) using various loss functions (L2, single-scale perceptual loss (PL), multiscale PL, weighted multiscale PL), and a patch-based method (PBM).

We propose an approach of 3D convolutional neural network to segment the prostate in MR images.

A 3D deep dense multi-path convolutional neural network that follows the framework of the encoder-decoder design is proposed.

We compared the performance of different Deep learning-convolutional neural network (DL-CNN) models for bladder cancer treatment response assessment based on transfer learning by freezing different DL-CNN layers and varying the DL-CNN structure.

Although recent advances in multiparametric magnetic resonance imaging (MRI) led to an increase in MRI-transrectal ultrasound (TRUS) fusion prostate biopsies, these are time consuming, laborious, and costly.

Muscle-invasive bladder cancer (MIBC) is the second most common genitourinary malignancy, and is associated with high morbidity and mortality. Recently, molecular subtypes of MIBC have been identified, which have important clinical implications.

Convolutional neural networks (CNNs) have been shown to be powerful tools to assist with object detection and-like a human observer-may be trained based on a relatively small cohort of reference subjects.

Magnetic resonance (MR) only radiation therapy for prostate treatment provides superior contrast for defining targets and organs-at-risk (OARs). This study aims to develop a deep learning model to leverage this advantage to automate the contouring process.

The pathologic diagnosis and Gleason grading of prostate cancer are time-consuming, error-prone, and subject to interobserver variability. Machine learning offers opportunities to improve the diagnosis, risk stratification, and prognostication of prostate cancer.

Registration and fusion of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) of prostate can provide guidance for prostate brachytherapy. However, accurate registration remains a challenging task due to the lack of ground-truth regarding voxel-level spatial correspondence, limited field of view, low contrast-to-noise ratio in TRUS.

Ultrasound (US)-guided high-dose-rate (HDR) prostate brachytherapy requests the clinicians to place HDR needles (catheters) into the prostate gland under transrectal US (TRUS) guidance in the operating room.

The purpose of this study is to develop a deep-learning based approach to simultaneously segment five pelvic organs including prostate, bladder, rectum, left and right femoral heads on cone-beam CT (CBCT), as required elements for prostate adaptive radiotherapy planning.

Multiparametric magnetic resonance imaging (mpMRI) of the prostate aids in early diagnosis of prostate cancer, but is difficult to interpret and subject to interreader variability. Our objective is to generate probability maps, overlaid on original mpMRI images to help radiologists identify where a cancer is suspected as a computer-aided diagnostic (CAD).

Clinical implementation of magnetic resonance imaging (MRI)-only radiotherapy requires a method to derive synthetic CT image (S-CT) for dose calculation. This study investigated the feasibility of building a deep convolutional neural network for MRI based S-CT generation and evaluate the dosimetric accuracy on prostate IMRT planning.

In this research, we exploit an image-based deep learning framework to distinguish three major subtypes of renal cell carcinoma (clear cell, papillary, and chromophobe) using images acquired with computed tomography (CT).