AIMC Topic: Liver Neoplasms

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Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research
Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill ...

Sparse Contribution Feature Selection and Classifiers Optimized by Concave-Convex Variation for HCC Image Recognition.

BioMed research international
Accurate classification of hepatocellular carcinoma (HCC) image is of great importance in pathology diagnosis and treatment. This paper proposes a concave-convex variation (CCV) method to optimize three classifiers (random forest, support vector mach...

Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network.

IEEE transactions on medical imaging
Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterativ...

Laparoscopic versus robotic surgery for hepatocellular carcinoma: the first 46 consecutive cases.

The Journal of surgical research
BACKGROUND: Hepatocellular carcinoma has a growing incidence worldwide, and represents a leading cause of death in patients with cirrhosis. Nowadays, minimally invasive approaches are spreading in every field of surgery and in liver surgery as well.

Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs.

Artificial intelligence in medicine
This paper presents a novel, fully automatic approach based on a fully convolutional network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a multi-channel fully convolutional network (MC-FCN) to segment liver tumors from...

Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

Computers in biology and medicine
Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) arch...

Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome.

Artificial intelligence in medicine
Finding new uses for existing drugs has become a new strategy for decades to treat more patients. Few traditional approaches consider the tissue specificities of diseases. Moreover, disease genes, drug targets and protein interaction (PPI) networks r...

Disease-free survival assessment by artificial neural networks for hepatocellular carcinoma patients after radiofrequency ablation.

Journal of the Formosan Medical Association = Taiwan yi zhi
BACKGROUND/PURPOSE: Radiofrequency ablation (RFA) provides an effective treatment for patients who exhibit early hepatocellular carcinoma (HCC) stages or are waiting for liver transplantation. It is important to assess patients after RFA. The goal of...