AIMC Topic: Image Processing, Computer-Assisted

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Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer.

Current urology reports
PURPOSE OF REVIEW: This review aims to explore the current state of research on the use of artificial intelligence (AI) in the management of prostate cancer. We examine the various applications of AI in prostate cancer, including image analysis, pred...

Segmentation of the aorta in systolic phase from 4D flow MRI: multi-atlas vs. deep learning.

Magma (New York, N.Y.)
OBJECTIVE: In the management of the aortic aneurysm, 4D flow magnetic resonance Imaging provides valuable information for the computation of new biomarkers using computational fluid dynamics (CFD). However, accurate segmentation of the aorta is requi...

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton ( H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acqui...

Technical note: Progressive deep learning: An accelerated training strategy for medical image segmentation.

Medical physics
BACKGROUND: Recent advancements in Deep Learning (DL) methodologies have led to state-of-the-art performance in a wide range of applications especially in object recognition, classification, and segmentation of medical images. However, training moder...

Segmentation of multiple Organs-at-Risk associated with brain tumors based on coarse-to-fine stratified networks.

Medical physics
BACKGROUND: Delineation of Organs-at-Risks (OARs) is an important step in radiotherapy treatment planning. As manual delineation is time-consuming, labor-intensive and affected by inter- and intra-observer variability, a robust and efficient automati...

X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module.

Journal of biomedical optics
SIGNIFICANCE: X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP...

Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues.

PloS one
Mechanical cues such as stresses and strains are now recognized as essential regulators in many biological processes like cell division, gene expression or morphogenesis. Studying the interplay between these mechanical cues and biological responses r...

EmoKbGAN: Emotion controlled response generation using Generative Adversarial Network for knowledge grounded conversation.

PloS one
Neural open-domain dialogue systems often fail to engage humans in long-term interactions on popular topics such as sports, politics, fashion, and entertainment. However, to have more socially engaging conversations, we need to formulate strategies t...

Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy.

International journal of computer assisted radiology and surgery
PURPOSE: Minimally invasive treatments for renal carcinoma offer a low rate of complications and quick recovery. One drawback of the use of computed tomography (CT) for needle guidance is the use of iodinated contrast agents, which require an increas...

Deep learning based classification of multi-label chest X-ray images via dual-weighted metric loss.

Computers in biology and medicine
-Thoracic disease, like many other diseases, can lead to complications. Existing multi-label medical image learning problems typically include rich pathological information, such as images, attributes, and labels, which are crucial for supplementary ...