Results 71 to 80 of about 169,019 (308)

Correction Learning for Medical Image Segmentation [PDF]

IEEE Access, 2019
Breast tumor segmentation is useful to diagnose breast cancer. However, challenges, such as intensity inhomogeneity and shadowing artifacts arise in this task. To address these two issues, this paper proposes a robust ultrasound image segmentation method based on correction learning. At first, a novel idea of correction learning is introduced.
Guang Zhang, Shuhao Dong, Hui Xu, Hongyu Zhang, Yongjian Wu 0001, Yanwei Zhang, Xiaoming Xi, Yilong Yin   +7 more
openaire   +2 more sources

Establishment of a humanized patient‐derived xenograft mouse model of high‐grade serous ovarian cancer for preclinical evaluation of combination immunotherapy

Molecular Oncology, EarlyView.
We have established a humanized orthotopic patient‐derived xenograft (Hu‐oPDX) mouse model of high‐grade serous ovarian cancer (HGSOC) that recapitulates human tumor–immune interactions. Using combined anti‐PD‐L1/anti‐CD73 immunotherapy, we demonstrate the model's improved biological relevance and enhanced translational value for preclinical ...
Luka Tandaric, Line Bjørge, Martine Rott Lode, Cecilie Fredvik Torkildsen, Pia Aehnlich, Rammah Elnour, Daniela Elena Costea, Lars Andreas Akslen, Liv Cecilie Vestrheim Thomsen, Emmet McCormack, Katrin Kleinmanns   +10 more
wiley   +1 more source

Deep learning for medical image segmentation: State-of-the-art advancements and challenges

Informatics in Medicine Unlocked
Image segmentation, a crucial process of dividing images into distinct parts or objects, has witnessed remarkable advancements with the emergence of deep learning (DL) techniques. The use of layers in deep neural networks, like object form recognition in
Md. Eshmam Rayed, S.M. Sajibul Islam, Sadia Islam Niha, Jamin Rahman Jim, Md Mohsin Kabir, M.F. Mridha   +5 more
doaj   +1 more source

Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoT

Journal of Applied Mathematics, 2014
Internet of Things (IoT) brings telemedicine a new chance. This enables the specialist to consult the patient’s condition despite the fact that they are in different places.
Seung-Hoon Chae, Daesung Moon, Deok Gyu Lee, Sung Bum Pan   +3 more
doaj   +1 more source

PENERAPAN CITRA TERKOMPRESI PADA SEGMENTASI CITRA MENGGUNAKAN ALGORITMA K-MEANS

JUTEI (Jurnal Terapan Teknologi Informasi), 2018
In the development of an image not only as a documentation of events. One area that requires image processing is in the field of medicine is radiology. In radiology there is a medical image required by doctors and researchers to be processed for patient ...
Angga Wijaya Kusuma, Rossy Lydia Ellyana
doaj   +1 more source

Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation

Applied Sciences, 2022
Image segmentation is a basic technology in the field of image processing and computer vision. Medical image segmentation is an important application field of image segmentation and plays an increasingly important role in clinical diagnosis and treatment.
Zan Li, Hong Zhang, Zhengzhen Li, Zuyue Ren   +3 more
doaj   +1 more source

Heterozygous loss‐of‐function alleles associate the conserved 3′‐5′ exoribonuclease EXOSC10 with hypersensitivity to the anticancer drug 5‐fluorouracil

Molecular Oncology, EarlyView.
EXOSC10, an essential nuclear RNA exosome‐associated 3′‐5′ exoribonuclease, is inhibited by the anticancer drug 5‐fluorouracil (5‐FU), and EXOSC10 depletion increases 5‐FU sensitivity. The colon‐cancer variant EXOSC10S402T, located in a proteolysis motif, is stable and nuclear but nonfunctional in vivo.
Radhika Sain, Yann Le Page, Frédéric Percevault, Emmanuelle Becker, Luc Negroni, Almudena Fernandez, Marta Cantero, Diego Muñoz‐Santos, Lluís Montoliu, Cyrille Garnier, Michael Primig   +10 more
wiley   +1 more source

Interactive Segmentation for Medical Images Using Spatial Modeling Mamba

Information
Interactive segmentation methods utilize user-provided positive and negative clicks to guide the model in accurately segmenting target objects. Compared to fully automatic medical image segmentation, these methods can achieve higher segmentation accuracy
Yuxin Tang, Yu Li, Hua Zou, Xuedong Zhang   +3 more
doaj   +1 more source

Improved Multistage Edge-Enhanced Medical Image Segmentation Network of U-Net [PDF]

Jisuanji gongcheng
Medical image segmentation accuracy plays a key role in clinical diagnosis and treatment. However, because of the complexity of medical images and diversity of target regions, existing medical image segmentation methods are limited to incomplete edge ...
HU Shuai, LI Hualing, HAO Dechen
doaj   +1 more source

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