Machine learning based liver disease diagnosis: A systematic review
The computer-based approach is required for the non-invasive detection of chronic liver
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …
GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification
M Frid-Adar, I Diamant, E Klang, M Amitai… - Neurocomputing, 2018 - Elsevier
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to
an enormous breakthrough in a wide range of computer vision tasks, primarily by using …
an enormous breakthrough in a wide range of computer vision tasks, primarily by using …
Computer-aided diagnosis of liver lesions using CT images: A systematic review
PV Nayantara, S Kamath, KN Manjunath… - Computers in Biology …, 2020 - Elsevier
Background Medical image processing has a strong footprint in radio diagnosis for the
detection of diseases from the images. Several computer-aided systems were researched in …
detection of diseases from the images. Several computer-aided systems were researched in …
MULAN: multitask universal lesion analysis network for joint lesion detection, tagging, and segmentation
When reading medical images such as a computed tomography (CT) scan, radiologists
generally search across the image to find lesions, characterize and measure them, and then …
generally search across the image to find lesions, characterize and measure them, and then …
Fully automatic liver and tumor segmentation from CT image using an AIM-Unet
The segmentation of the liver is a difficult process due to the changes in shape, border, and
density that occur in each section in computed tomography (CT) images. In this study, the …
density that occur in each section in computed tomography (CT) images. In this study, the …
Combining convolutional and recurrent neural networks for classification of focal liver lesions in multi-phase CT images
D Liang, L Lin, H Hu, Q Zhang, Q Chen… - … Image Computing and …, 2018 - Springer
Computer-aided diagnosis (CAD) systems are useful for assisting radiologists with clinical
diagnoses by classifying focal liver lesions (FLLs) based on multi-phase computed …
diagnoses by classifying focal liver lesions (FLLs) based on multi-phase computed …
Holistic and comprehensive annotation of clinically significant findings on diverse CT images: learning from radiology reports and label ontology
In radiologists' routine work, one major task is to read a medical image, eg, a CT scan, find
significant lesions, and describe them in the radiology report. In this paper, we study the …
significant lesions, and describe them in the radiology report. In this paper, we study the …
Artificial intelligence in radiology
The interest in artificial intelligence (AI) has ballooned within radiology in the past few years
primarily due to notable successes of deep learning. With the advances brought by deep …
primarily due to notable successes of deep learning. With the advances brought by deep …
Classification of focal liver lesions in CT images using convolutional neural networks with lesion information augmented patches and synthetic data augmentation
Purpose We propose a deep learning method that classifies focal liver lesions (FLLs) into
cysts, hemangiomas, and metastases from portal phase abdominal CT images. We propose …
cysts, hemangiomas, and metastases from portal phase abdominal CT images. We propose …
Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT …
Y Xu, L Lin, H Hu, D Wang, W Zhu, J Wang… - International journal of …, 2018 - Springer
Purpose The bag of visual words (BoVW) model is a powerful tool for feature representation
that can integrate various handcrafted features like intensity, texture, and spatial information …
that can integrate various handcrafted features like intensity, texture, and spatial information …