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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
2007 1
2008 3
2011 3
2013 4
2014 3
2015 4
2016 1
2017 8
2018 8
2019 15
2020 19
2021 33
2022 38
2023 45
2024 18

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178 results

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Page 1
Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma.
Harding-Theobald E, Louissaint J, Maraj B, Cuaresma E, Townsend W, Mendiratta-Lala M, Singal AG, Su GL, Lok AS, Parikh ND. Harding-Theobald E, et al. Aliment Pharmacol Ther. 2021 Oct;54(7):890-901. doi: 10.1111/apt.16563. Epub 2021 Aug 12. Aliment Pharmacol Ther. 2021. PMID: 34390014 Free PMC article. Review.
Computational "radiomic" techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology. ...Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging sk …
Computational "radiomic" techniques extract biomarker information from images which can be used to improve diagnosis and predi …
Collaborative multi-feature extraction and scale-aware semantic information mining for medical image segmentation.
Zhang R, He Z, Zhu J, Yuan X, Huang G, Pun CM, Peng J, Lin J, Zhou J. Zhang R, et al. Phys Med Biol. 2022 Oct 14;67(20). doi: 10.1088/1361-6560/ac95f5. Phys Med Biol. 2022. PMID: 36170875
Objective.In recent years, methods based on U-shaped structure and skip connection have achieved remarkable results in many medical semantic segmentation tasks. However, the information integration capability of this structure is still limited due to the incompatibility of …
Objective.In recent years, methods based on U-shaped structure and skip connection have achieved remarkable results in many medical semantic …
Eres-UNet++: Liver CT image segmentation based on high-efficiency channel attention and Res-UNet+.
Li J, Liu K, Hu Y, Zhang H, Heidari AA, Chen H, Zhang W, Algarni AD, Elmannai H. Li J, et al. Comput Biol Med. 2023 May;158:106501. doi: 10.1016/j.compbiomed.2022.106501. Epub 2023 Jan 10. Comput Biol Med. 2023. PMID: 36635120
Computerized tomography (CT) is of great significance for the localization and diagnosis of liver cancer. Many scholars have recently applied deep learning methods to segment CT images of liver and liver tumors. Unlike natural images …
Computerized tomography (CT) is of great significance for the localization and diagnosis of liver cancer. Many scholars have r …
Personalized Liver Cancer Risk Prediction Using Big Data Analytics Techniques with Image Processing Segmentation.
Jain A, Nadeem A, Majdi Altoukhi H, Jamal SS, Atiglah HK, Elwahsh H. Jain A, et al. Comput Intell Neurosci. 2022 Mar 28;2022:8154523. doi: 10.1155/2022/8154523. eCollection 2022. Comput Intell Neurosci. 2022. PMID: 35387251 Free PMC article.
This approach is utilised for the development of novel drugs, and it is a time-consuming procedure that includes the docking of ligands in several databases in order to build the protein receptor. The proposed work is divided into two modules: image processing-based …
This approach is utilised for the development of novel drugs, and it is a time-consuming procedure that includes the docking of ligands in s …
mfeeU-Net: A multi-scale feature extraction and enhancement U-Net for automatic liver segmentation from CT Images.
Liu J, Yan Z, Zhou C, Shao L, Han Y, Song Y. Liu J, et al. Math Biosci Eng. 2023 Feb 21;20(5):7784-7801. doi: 10.3934/mbe.2023336. Math Biosci Eng. 2023. PMID: 37161172 Free article.
Medical image segmentation of the liver is an important prerequisite for clinical diagnosis and evaluation of liver cancer. For automatic liver segmentation from Computed Tomography (CT) images, we proposed a Multi-scale …
Medical image segmentation of the liver is an important prerequisite for clinical diagnosis and evaluation of liver …
Liver tumor segmentation and classification using FLAS-UNet++ and an improved DenseNet.
Peng Q, Yan Y, Qian L, Suo S, Guo Y, Xu J, Wang Y. Peng Q, et al. Technol Health Care. 2022;30(6):1475-1487. doi: 10.3233/THC-213655. Technol Health Care. 2022. PMID: 35661035
METHODS: Firstly, the liver tumor is segmented from the original CT images by a tumor segmentation network, UNet++ with fusion loss and atrous spatial pyramid pooling (FLAS-UNet++). ...CONCLUSIONS: In order to solve existing problems of liver tu …
METHODS: Firstly, the liver tumor is segmented from the original CT images by a tumor segmentation network, UNet …
Spatial feature fusion convolutional network for liver and liver tumor segmentation from CT images.
Liu T, Liu J, Ma Y, He J, Han J, Ding X, Chen CT. Liu T, et al. Med Phys. 2021 Jan;48(1):264-272. doi: 10.1002/mp.14585. Epub 2020 Nov 27. Med Phys. 2021. PMID: 33159809
PURPOSE: The accurate segmentation of liver and liver tumors from CT images can assist radiologists in decision-making and treatment planning. ...However, due to the diversity of shape, volume, and image intensity, the segmentation is sti …
PURPOSE: The accurate segmentation of liver and liver tumors from CT images can assist radiologists in decision- …
Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases.
Saber R, Henault D, Messaoudi N, Rebolledo R, Montagnon E, Soucy G, Stagg J, Tang A, Turcotte S, Kadoury S. Saber R, et al. J Transl Med. 2023 Jul 27;21(1):507. doi: 10.1186/s12967-023-04175-7. J Transl Med. 2023. PMID: 37501197 Free PMC article.
Particularly, CD73 is an ectonucleotidase that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative int …
Particularly, CD73 is an ectonucleotidase that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be b …
MS-FANet: Multi-scale feature attention network for liver tumor segmentation.
Chen Y, Zheng C, Zhang W, Lin H, Chen W, Zhang G, Xu G, Wu F. Chen Y, et al. Comput Biol Med. 2023 Sep;163:107208. doi: 10.1016/j.compbiomed.2023.107208. Epub 2023 Jun 26. Comput Biol Med. 2023. PMID: 37421737
Accurate segmentation of liver tumors is a prerequisite for early diagnosis of liver cancer. ...The dual-path feature (DF) filter and dense upsampling (DU) are introduced in the feature reduction process to reduce effective features …
Accurate segmentation of liver tumors is a prerequisite for early diagnosis of liver cancer. ...The dual-path …
Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers.
Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Tabari A, et al. Cancers (Basel). 2022 Dec 22;15(1):63. doi: 10.3390/cancers15010063. Cancers (Basel). 2022. PMID: 36612061 Free PMC article. Review.
Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroend …
Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the …
178 results