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Automated quantitative assessment of oncological disease progression using deep learning

Yiftach Barash, Eyal Klang
2019 Annals of Translational Medicine  
Since 2012, CNN algorithms continue to improve, reaching human level in several image analysis tasks. A more recent deep learning advancement are generative adversarial networks (GANs) (19) .  ...  In the field of oncological-radiology, they have the potential to improve patient care by facilitating precision medicine. Acknowledgments None.  ... 
doi:10.21037/atm.2019.12.101 pmid:32016097 pmcid:PMC6976497 fatcat:sbepkkw5ajapdnmx7acpcvcyeu

GenoMed4All - Slide deck

AUSTRALO
2021 Zenodo  
advanced generative models such as Variational Autoencoders and Generative Adversarial Networks Optimal fusion architectures using heterogeneous data as a combination of feedforward, convolutional and  ...  Unleashing the power of AI Exploring new models in genomics for precision medicine AI-based services for clinical support GenoMed4All will deploy 'white box' AI models in 3 real-world pilots for common  ... 
doi:10.5281/zenodo.4629794 fatcat:bzprueyeangxdjsunllin67emq

GenoMed4All - Slide deck

AUSTRALO
2021 Zenodo  
advanced generative models such as Variational Autoencoders and Generative Adversarial Networks Optimal fusion architectures using heterogeneous data as a combination of feedforward, convolutional and  ...  Unleashing the power of AI Exploring new models in genomics for precision medicine AI-based services for clinical support GenoMed4All will deploy 'white box' AI models in 3 real-world pilots for common  ... 
doi:10.5281/zenodo.4650067 fatcat:l3uionjmfzhvrhulblcgovw4ci

Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model [article]

Sophie-Camille Hogue, Flora Chen, Geneviève Brassard, Denis Lebel, Jean-François Bussières, Audrey Durand, Maxime Thibault
2020 arXiv   pre-print
We finally used this autoencoder in an adversarial training model adapted from GANomaly (Akçay et al., 2018) , an autoencoder-based generative adversarial network.  ...  ± 0.6% in NICU, 16.3 ± 0.8% in general pediatrics and 20.6 ± 1.3% in oncology.  ... 
arXiv:2011.01925v1 fatcat:z54ythyqtfg55fpzkthgmydjjy

A Survey on Deep Learning for Precision Oncology

Ching-Wei Wang, Muhammad-Adil Khalil, Nabila Puspita Firdi
2022 Diagnostics  
Deep learning has become the main method for precision oncology.  ...  This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years.  ...  Generative Adversarial Network (GAN) The Generative Adversarial Network (GAN) was proposed in 2014 by Goodfellow et al. [165] .  ... 
doi:10.3390/diagnostics12061489 pmid:35741298 pmcid:PMC9222056 fatcat:2qgvdz4x7rejxkwgoascxk77ke

Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters

Michela Paganini, Luke de Oliveira, Benjamin Nachman
2018 Physical Review Letters  
We therefore introduce a deep neural network-based generative model to enable high-fidelity, fast, electromagnetic calorimeter simulation.  ...  The modeling of detectors and the precise description of particle cascades as they interact with the material in the calorimeter are the most computationally demanding steps in the simulation pipeline.  ...  -This Letter demonstrates that the generative adversarial network technology represents a powerful new tool for efficient simulation.  ... 
doi:10.1103/physrevlett.120.042003 pmid:29437460 fatcat:e6sxsqze7baahfaottkkion2ru

Adversary-aware Multimodal Neural Networks for Cancer Susceptibility Prediction from Multi-omics Data

Md. Rezaul Karim, Tanhim Islam, Christoph Lange, Dietrich Rebholz-Schuhmann, Stefan Decker
2022 IEEE Access  
Providing accurate diagnosis for cancer is a challenging problem in precision oncology.  ...  We study different adversarial attacks scenarios and take both proactive and reactive measures (e.g., adversarial retraining and identification of adversarial inputs).  ...  Adversary-aware Multimodal Neural Networks for Cancer Diagnosis (a) Precision plot: if the fraction positive increases with the predicted probability (b) Lift curve: the percentage of positive classes  ... 
doi:10.1109/access.2022.3175816 fatcat:fdpdj6i7xzherkz5tklhftazbm

Adversary-Aware Multimodal Neural Networks for Cancer Susceptibility Prediction From Multiomics Data

Md. Rezaul Karim, Tanhim Islam, Christoph Lange-Bever, Dietrich Rebholz-Schuhmann, Stefan Josef Decker
2022 IEEE access 10  
Providing accurate diagnosis for cancer is a challenging problem in precision oncology.  ...  To make the MCAE model robust to adversaries and to provide consistent diagnosis, we formulate robustness as a property, such that predictions remain stable with regard to small variations in the input  ...  Examples include adversarial detecting, input reconstruction, and network verification. The latter is about making an ML more robust before an adversary generates and introduces an attack.  ... 
doi:10.18154/rwth-2022-06375 fatcat:kugca7gol5fbrmwb34m33lpfve

ETH Zurich at TREC Precision Medicine 2017

Negar Foroutan Eghlidi, Jannick Griner, Nicolas Mesot, Leandro von Werra, Carsten Eickhoff
2017 Text Retrieval Conference  
feed-forward networks trained on PubMed and NCBI information but also relying on generative adversarial methods to determine the likelihood of co-occurrence of various mutations within the same patient  ...  This paper describes ETH Zurich's submission to the TREC 2017 Precision Medicine (PM) track.  ...  In this paper, we present a modular patient-centric information retrieval system for use in precision oncology settings.  ... 
dblp:conf/trec/EghlidiGMWE17 fatcat:geq52v55e5fpvbptjwfw7ksuay

The Use of Artificial Intelligence on Segmental Volumes, Constructed from MRI and CT Images, in the Diagnosis and Staging of Cervical Cancers and Thyroid Cancers: A Study Protocol for a Randomized Controlled Trial

Tudor Florin Ursuleanu, Andreea Roxana Luca, Liliana Gheorghe, Roxana Grigorovici, Stefan Iancu, Maria Hlusneac, Cristina Preda, Alexandru Grigorovici
2021 Journal of Biomedical Science and Engineering  
We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven  ...  in segmental volumetric constructions to generate 3D images from MRI/CT.  ...  Artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN) has proven to be effective in representing complex data distributions [9] , as we do in the present study.  ... 
doi:10.4236/jbise.2021.146025 fatcat:hpxt2y3yojginob54t2tmfysfe

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics [article]

Hossein Sharifi Noghabi, Shuman Peng, Olga Zolotareva, Colin C Collins, Martin Ester
2020 bioRxiv   pre-print
Results: We propose Adversarial Inductive Transfer Learning (AITL), a deep neural network method for addressing discrepancies in input and output space between the pre-clinical and clinical datasets.  ...  Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately.  ...  We conclude that AITL may be beneficial in pharmacogenomics, a crucial task in precision oncology. .  ... 
doi:10.1101/2020.01.24.918953 fatcat:65dktmiy4nblfg5ensl6fs7ggi

AI applications in robotics, precision medicine, and medical image analysis: an overview and future trends

Tetiana Habuza, Alramzana Nujum Navaz, Faiza Hashim, Fady Alnajjar, Nazar Zaki, Mohamed Adel Serhani, Yauhen Statsenko
2021 Informatics in Medicine Unlocked  
AI in precision medicine and oncology allows for risk stratification due to genomics aberrations discovered on molecular testing. To summarize, AI cannot substitute a medical doctor.  ...  Robotics has taken huge leaps in improving the healthcare services in a variety of medical sectors, including oncology and surgical interventions.  ...  Conflict of interests All authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  ... 
doi:10.1016/j.imu.2021.100596 fatcat:f3f7eenvw5hpnik4xx7divn3nq

Cone-Beam Computed Tomography (CBCT) Segmentation by Adversarial Learning Domain Adaptation [chapter]

Xiaoqian Jia, Sicheng Wang, Xiao Liang, Anjali Balagopal, Dan Nguyen, Ming Yang, Zhangyang Wang, Jim Xiuquan Ji, Xiaoning Qian, Steve Jiang
2019 Lecture Notes in Computer Science  
Our adversarial learning domain adaptation is integrated with the CBCT segmentation network training with the designed loss functions.  ...  Our experiments on the bladder images from Radiation Oncology clinics at the University of Texas Southwestern Medical School (UTSW) have shown that our CBCT segmentation with adversarial learning domain  ...  Generative Adversarial Network In the past few years, generative adversarial network (GAN) has gained more attention in computer vision because of their significant performance in image generation, image  ... 
doi:10.1007/978-3-030-32226-7_63 fatcat:7zbhw3xgw5b63bjstdbbtwqulu

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks

Bilal Ahmad, Jun Sun, Qi You, Vasile Palade, Zhongjie Mao
2022 Biomedicines  
We combine two generative models in the proposed framework: variational autoencoders (VAEs) and generative adversarial networks (GANs).  ...  The output of this swapped network is a noise vector that has information of the image manifold, and the cascaded generative adversarial network samples the input from this informative noise vector instead  ...  Generative Adversarial Networks Generative adversarial networks (GANs) have been one of the most impressive advancements in generative approaches.  ... 
doi:10.3390/biomedicines10020223 pmid:35203433 pmcid:PMC8869455 fatcat:4yd6xiwavrc27lpmkzwjzh2btu

Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection

Qiyang Ge, Xuelin Huang, Shenying Fang, Shicheng Guo, Yuanyuan Liu, Wei Lin, Momiao Xiong
2020 Frontiers in Genetics  
The traditional methods are difficult to apply to precision oncology. Artificial intelligence (AI) is a powerful tool for precision oncology.  ...  To overcome these limitations, we modify conditional generative adversarial networks (MCGANs) to allow estimation of individualized effects of any types of treatments including binary, categorical and  ...  AI and causal inferences are becoming a driving force for innovation in precision oncology (Seyhan and Carini, 2019) .  ... 
doi:10.3389/fgene.2020.585804 pmid:33362849 pmcid:PMC7759680 fatcat:mpku3h3lebgxzowdnzyt2b5jqm
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