Results 91 to 100 of about 5,846,406 (312)

Metastatic niche shaped by host factors influences disseminated cancer cell fate

FEBS Letters, EarlyView.
Metastasis is shaped not only by cancer cells but also by the environments they encounter. This review explores how factors such as aging, diet, the microbiome, lifestyle, and environmental exposures remodel organ‐specific niches in the lung, liver, bone, and brain, influencing where metastatic cells survive, remain dormant, or grow, and ultimately ...
Gwennan Delyth Ward, Álvaro Ruiz‐Tabas, Patricia Altea‐Manzano   +2 more
wiley   +1 more source

Taming the reservoir : feedforward training for recurrent neural networks

, 2012
Recurrent neural networks are successfully used for tasks like time series processing and system identification. Many of the approaches to train these networks, however, are often regarded as too slow, too complicated, or both.
Obst, Oliver, Obst, Oliver (R18459), Martin Riedmiller, Riedmiller, Martin, Oliver Obst   +4 more
core   +1 more source

Backdoor Attack on Deep Neural Networks in Perception Domain

, 2023
As deep neural networks (DNNs) are widely deployed in various applications, the security of pretrained DNNs is crucial since backdoors can be introduced through poisoned training. A backdoored DNN model works properly when benign inputs are provided, but
Mo, X, Zhang, LY, Gao, S, Luo, W, Sun, N
core   +1 more source

Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability

Nature Communications
Deep neural networks have revolutionized several domains, including autonomous driving, cancer detection, and drug design, and are the foundation for massive artificial intelligence models.
Zhenjia Chen, Zhenyuan Lin, Ji Yang, Cong Chen, Di Liu, Liuting Shan, Yuanyuan Hu, Tailiang Guo, Huipeng Chen   +8 more
doaj   +1 more source

Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images

Algorithms, 2019
Finetuning pre-trained deep neural networks (DNN) delicately designed for large-scale natural images may not be suitable for medical images due to the intrinsic difference between the datasets.
Qingge Ji, Jie Huang, Wenjie He, Yankui Sun   +3 more
doaj   +1 more source

Tumour–host interactions in Drosophila: mechanisms in the tumour micro‐ and macroenvironment

Molecular Oncology, EarlyView.
This review examines how tumour–host crosstalk takes place at multiple levels of biological organisation, from local cell competition and immune crosstalk to organism‐wide metabolic and physiological collapse. Here, we integrate findings from Drosophila melanogaster studies that reveal conserved mechanisms through which tumours hijack host systems to ...
José Teles‐Reis, Tor Erik Rusten
wiley   +1 more source

Deep Neural Networks with Multistate Activation Functions [PDF]

, 2015
We propose multistate activation functions (MSAFs) for deep neural networks (DNNs). These MSAFs are new kinds of activation functions which are capable of representing more than two states, including the N-order MSAFs and the symmetrical MSAF.
Su, Kaile, Ke, Dengfeng, Xu, Yanyan, Cai, Chenghao   +3 more
core   +1 more source

Metastasis on pause: How dormant tumor cells stay hidden within the tumor microenvironment and evade immune surveillance

Molecular Oncology, EarlyView.
Dormant cancer cells can hide in distant organs for years, evading treatment and the immune system. This review highlights how signals from the surrounding tissue and immune environment keep these cells inactive or trigger their reawakening. Understanding these mechanisms may help develop therapies to eliminate or control dormant cells and prevent ...
Kanishka Tiwary, Jose Javier Bravo‐Cordero   +1 more
wiley   +1 more source

Deep Neural Networks Ensemble for Lung Nodule Detection on Chest CT Scans

, 2021
Identifying and diagnosing as early as possible malignant lung nodules is essential to reduce the mortality of lung cancer patients. Radiologists employ computer tomography scan to detect cancer in the body and track its growth.
Aversano L., Ardimento P., Bernardi M. L., Cimitile M.   +3 more
core   +1 more source

Concolic testing for deep neural networks [PDF]

Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, 2018
Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. This paper presents the first concolic testing approach for Deep Neural Networks (DNNs). More specifically, we formalise coverage criteria for DNNs that have been studied in the literature, and then develop a coherent method for ...
Youcheng Sun, Min Wu 0011, Wenjie Ruan, Xiaowei Huang 0001, Marta Kwiatkowska, Daniel Kroening   +5 more
openaire   +7 more sources