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Liver Neoplasms

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Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI.

Korean journal of radiology
OBJECTIVE: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging...

Deep learning for evaluation of microvascular invasion in hepatocellular carcinoma from tumor areas of histology images.

Hepatology international
BACKGROUND: Microvascular invasion (MVI) is essential for the management of hepatocellular carcinoma (HCC). However, MVI is hard to evaluate in patients without sufficient peri-tumoral tissue samples, which account for over a half of HCC patients.

Multimodal Imaging under Artificial Intelligence Algorithm for the Diagnosis of Liver Cancer and Its Relationship with Expressions of EZH2 and p57.

Computational intelligence and neuroscience
OBJECTIVE: It aimed to explore the diagnostic efficacy of multimodal ultrasound images based on mask region with convolutional neural network (M-RCNN) segmentation algorithm for small liver cancer and analyze the expression of zeste gene enhancer hom...

Automatic Segmentation of Magnetic Resonance Images of Severe Patients with Advanced Liver Cancer and the Molecular Mechanism of Emodin-Induced Apoptosis of HepG2 Cells under the Deep Learning.

Journal of healthcare engineering
To improve the accuracy of clinical diagnosis of severe patients with advanced liver cancer and enhance the effect of chemotherapy treatment, the U-Net model was optimized by introducing the batch normalization (BN) layer and the dropout layer, and t...

Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts.

Journal of gastroenterology
BACKGROUND: Ultrasonography (US) is widely used for the diagnosis of liver tumors. However, the accuracy of the diagnosis largely depends on the visual perception of humans. Hence, we aimed to construct artificial intelligence (AI) models for the dia...

A Liver Damage Prediction Using Partial Differential Segmentation with Improved Convolutional Neural Network.

Journal of healthcare engineering
BACKGROUND: The liver is one of the most significant and most essential organs in the human body. It is divided into two granular lobes, one on the right and one on the left, connected by a bile duct. The liver is essential in the removal of waste pr...

Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy.

Radiation oncology (London, England)
BACKGROUND: In infrared reflective (IR) marker-based hybrid real-time tumor tracking (RTTT), the internal target position is predicted with the positions of IR markers attached on the patient's body surface using a prediction model. In this work, we ...

Deep Learning-Based Classification of Hepatocellular Nodular Lesions on Whole-Slide Histopathologic Images.

Gastroenterology
BACKGROUND & AIMS: Hepatocellular nodular lesions (HNLs) constitute a heterogeneous group of disorders. Differential diagnosis among these lesions, especially high-grade dysplastic nodules (HGDNs) and well-differentiated hepatocellular carcinoma (WD-...

Compound W-Net with Fully Accumulative Residual Connections for Liver Segmentation Using CT Images.

Computational and mathematical methods in medicine
Computed tomography (CT) is a common modality for liver diagnosis, treatment, and follow-up process. Providing accurate liver segmentation using CT images is a crucial step towards those tasks. In this paper, we propose a stacked 2-U-Nets model with ...