Results 71 to 80 of about 931,369 (334)

AutoSegNet: An Automated Neural Network for Image Segmentation

IEEE Access, 2020
Neural Architecture Search (NAS) has drawn significant attention as a tool for automatically constructing deep neural networks. The generated neural networks are mainly applied for image classification, and natural language processing. However, there are
Zhimin Xu, Si Zuo, Edmund Y. Lam, Byoungho Lee, Ni Chen   +4 more
doaj   +1 more source

Flow‐based immunomagnetic enrichment of circulating tumor cells from diagnostic leukapheresis product

Molecular Oncology, EarlyView.
The number of circulating tumor cells obtained from prostate cancer patients was increased approximately 5‐fold compared to regular CellSearch when processing 2 mL diagnostic leukapheresis material aliquots and increased by 44‐fold when processing 20 mL DLA aliquots using the flow enrichment target capture Halbach‐array.
Michiel Stevens, Anouk Mentink, Frank A. W. Coumans, Eshwari Dathathri, Khrystany T. Isebia, Jaco Kraan, Ronald de Wit, John W. M. Martens, Leon W. M. M. Terstappen   +8 more
wiley   +1 more source

Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model [PDF]

arXiv, 2023
The Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images.
arxiv  

Adaptive scale segmentation algorithm for polarimetric SAR image

The Journal of Engineering, 2019
Polarimetric SAR (PolSAR) data can be characterised by scattering matrix, which contains four elements. It is difficult to merge all the elements of the scattering matrix for segmentation.
Yifan Xu, Aifang Liu, Long Huang
doaj   +1 more source

Cellular liquid biopsy provides unique chances for disease monitoring, preclinical model generation and therapy adjustment in rare salivary gland cancer patients

Molecular Oncology, EarlyView.
We quantified and cultured circulating tumor cells (CTCs) of 62 patients with various cancer types and generated CTC‐derived tumoroid models from two salivary gland cancer patients. Cellular liquid biopsy‐derived information enabled molecular genetic assessment of systemic disease heterogeneity and functional testing for therapy selection in both ...
Nataša Stojanović Gužvić, Florian Lüke, Steffi Treitschke, Andrea Coluccio, Martin Hoffmann, Giancarlo Feliciello, Adithi Ravikumar Varadarajan, Xin Lu, Kathrin Weidele, Catherine Botteron, Silvia Materna–Reichelt, Felix Keil, Katja Evert, Florian Weber, Thomas Schamberger, Michael Althammer, Jirka Grosse, Dirk Hellwig, Christian Schulz, Stephan Seitz, Peter Ugocsai, Anke Schlenska‐Lange, Roman Mayr, Ulrich Kaiser, Wolfgang Dietmaier, Bernhard Polzer, Jens Warfsmann, Kamran Honarnejad, Tobias Pukrop, Daniel Heudobler, Christoph A. Klein, Christian Werno   +31 more
wiley   +1 more source

Image Segmentation Algorithms Overview [PDF]

arXiv, 2017
The technology of image segmentation is widely used in medical image processing, face recognition pedestrian detection, etc. The current image segmentation techniques include region-based segmentation, edge detection segmentation, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc.
arxiv  

Crosstalk between gut microbiota and tumor: tumors could cause gut dysbiosis and metabolic imbalance

Molecular Oncology, EarlyView.
In this research, we analyzed the relationship between gut microbiota and tumor. We discovered that both subcutaneous and metastatic tumors would alter the composition and metabolic function of gut microbiota. Meanwhile, fecal microbiota transplantation also indicated the anti‐tumor role of the gut microbiota, revealing the crosstalk between tumor and ...
Siyuan Zhang, Haimei Wen, Ying Chen, Jingya Ning, Di Hu, Yujiao Dong, Chenyu Yao, Bo Yuan, Shuanying Yang   +8 more
wiley   +1 more source

SRCNet: Seminal Representation Collaborative Network for Marine Oil Spill Segmentation [PDF]

arXiv, 2023
Effective oil spill segmentation in Synthetic Aperture Radar (SAR) images is critical for marine oil pollution cleanup, and proper image representation is helpful for accurate image segmentation. In this paper, we propose an effective oil spill image segmentation network named SRCNet by leveraging SAR image representation and the training for oil spill
arxiv  

Lung image segmentation via generative adversarial networks

Frontiers in Physiology
IntroductionLung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment.MethodsThis paper explores the lung CT image segmentation method by generative adversarial networks.
Jiaxin Cai, Hongfeng Zhu, Siyu Liu, Yang Qi, Rongshang Chen   +4 more
doaj   +1 more source

A New Nonlinear Diffusion Equation Model for Noisy Image Segmentation

Advances in Mathematical Physics, 2016
Image segmentation and image denoising are two important and fundamental topics in the field of image processing. Geometric active contour model based on level set method can deal with the problem of image segmentation, but it does not consider the ...
Bo Chen, Xiao-Hui Zhou, Li-Wei Zhang, Jie Wang, Wei-Qiang Zhang, Chen Zhang   +5 more
doaj   +1 more source