IOTG-01. Computational Neurosurgery in Brain Tumors: A paradigm shift on the use of Artificial Intelligence and Connectomics in pre- and intra-operative imaging

Abstract Computational Neurosurgery is a novel field where computational modeling and artificial intelligence (AI) are used to analyze diseases of neurosurgical interest. Our aim is to apply AI models to brain tumor (BT) images to a) automatically segment BTs on pre-operative MRI, b) predict the genetic subtype of glioma on intra- and post-operative histological specimens; and c) predict the extent of resection according to connectomics data. For the segmentation task, we used 510 BT images to train a deep learning (DL) model for automatic segmentation of the tumors’ edges and comparison of the AI-generated masks versus experts’ consensus (quantified by means of the dice score). For the histopathology task, we digitalized 266 hematoxylin/eosin slides of gliomas (including 130 IDH-wildtype and 136 IDH-mutant) and applied a DL architecture to predict the IDH genetic status, then validated by immunohistochemistry and genetic sequencing. The datasets were also augmented by generating synthetic glioma images by means of a Generative Adversarial Network methodology. The resection of 10 BTs was also customized according to connectomics data. In the segmentation experiment, we reached a dice score of ~0.9 (out of 1.0), while further demonstrating that only the T1, T1 after gadolinium, and FLAIR sequences are necessary for accurate automatic segmentation. In the histopathology task, we were able to predict the genetic status with accuracy between 76% and 95% using the DL model. The machine learning-based connectome analysis allowed us to perform safe supramaximal resection. We have shown the robustness of applying AI methodology or the automatic segmentation of BTs in MR imaging. Moreover, we have also shown that AI can be used to predict the genetic status, specifically, IDH, in histopathology images of gliomas. Our results support the use of AI in the clinical scenario for a fast and objective computerized characterization of patients affected by BTs.

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GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS

Electronics ◽

10.3390/electronics11020245 ◽

2022 ◽

Vol 11 (2) ◽

pp. 245

Author(s):

Konstantinos G. Liakos ◽

Georgios K. Georgakilas ◽

Fotis C. Plessas ◽

Paris Kitsos

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Power Analysis ◽

Public Libraries ◽

Data Sets ◽

Hardware Trojan ◽

Generative Adversarial Network ◽

Data Set ◽

Encrypted Data ◽

Adversarial Network

A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few samples of benchmarks that have been created from circuits large in size. Thus, it is difficult, based on these data, to develop robust ML-based models against HTs. In this paper, we propose a new deep learning (DL) tool named Generative Artificial Intelligence Netlists SynthesIS (GAINESIS). GAINESIS is based on the Wasserstein Conditional Generative Adversarial Network (WCGAN) algorithm and area–power analysis features from the GLN phase and synthesizes new normal and infected circuit samples for this phase. Based on our GAINESIS tool, we synthesized new data sets, different in size, and developed and compared seven ML classifiers. The results demonstrate that our new generated data sets significantly enhance the performance of ML classifiers compared with the initial data set of Trust-HUB.

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Characterization of Background-Anomaly Separability With Generative Adversarial Network for Hyperspectral Anomaly Detection

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.3013022 ◽

2020 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Jiaping Zhong ◽

Weiying Xie ◽

Yunsong Li ◽

Jie Lei ◽

Qian Du

Keyword(s):

Anomaly Detection ◽

Generative Adversarial Network ◽

Adversarial Network

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Super-Resolution and Self-Attention with Generative Adversarial Network for Improving Malignancy Characterization of Hepatocellular Carcinoma

2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi45749.2020.9098705 ◽

2020 ◽

Author(s):

Yunling Li ◽

Hui Huang ◽

Lijuan Zhang ◽

Guangyi Wang ◽

Honglai Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Super Resolution ◽

Generative Adversarial Network ◽

Adversarial Network

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AI detection of M. Tuberculosis pathogens using Generative Adversarial Network (GAN) analyses

European Journal of Public Health ◽

10.1093/eurpub/ckaa165.320 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

M Dedicatoria ◽

S Klaus ◽

R Case ◽

S Na ◽

E Ludwick ◽

...

Keyword(s):

Artificial Intelligence ◽

Computer Vision ◽

Mobile Computing ◽

Initial Step ◽

Rapid Identification ◽

Training Data ◽

Outbreak Detection ◽

Outbreak Response ◽

Generative Adversarial Network ◽

Adversarial Network

Abstract Background Rapid identification of pathogens is critical to outbreak detection and sentinel surveillance; however most diagnoses are made in laboratory settings. Advancements in artificial intelligence (AI) and computer vision offer unprecedented opportunities to facilitate detection and reduce response time in field settings. An initial step is the creation of analysis algorithms for offline mobile computing applications. Methods AI models to identify objects using computer vision are typically “trained” on previously labeled images. The scarcity of labeled image-libraries creates a bottleneck, requiring thousands of labor hours to annotate images by hand to create “training data.” We describe the applicability of Generative Adversarial Network (GAN) methods to amass sufficient training data with minimal manual input. Results Our AI models are built with a performance score of 0.84-0.93 for M. Tuberculosis, a measure of the AI model's accuracy using precision and recall. Our results demonstrate that our GAN pipeline boosts model robustness and learnability of sparse open source data. Conclusions The use of labeled training data to identify M. Tuberculosis developed using our GAN pipeline techniques demonstrates the potential for rapid identification of known pathogens in field settings. Our work paves the way for the development of offline mobile computing applications to identify pathogens outside of a laboratory setting. Advancements in artificial intelligence (AI) and computer vision offer unprecedented opportunities to decrease detection time in field settings by combining these technologies. Further development of these capabilities can improve time-to-detection and outbreak response significantly. Key messages Rapidly deploy AI detectors to aid in disease outbreak and surveillance. Our concept aligns with deploying responsive alerting capabilities to address dynamic threats in low resource, offline computing environs.

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Computational analysis of transition temperatures (Tts) of proteins fused to elastin-like polypeptide (ELP): deep fake evaluation of proteins, linkers, and trailers features

10.1101/2021.12.29.474407 ◽

2021 ◽

Author(s):

Mohammad Davoud Ghafari ◽

Iraj Rasooli ◽

Khosro Khajeh ◽

Bahareh Dabirmanesh ◽

Mohammadreza Ghafari ◽

...

Keyword(s):

Artificial Intelligence ◽

Phase Transition ◽

Transition Temperature ◽

Computational Analysis ◽

Large Data ◽

High Accuracy ◽

Sequence Length ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Protein Feature

The phase transition temperature (Tt) prediction of the Elastin-like polypeptides (ELPs) is not trivial because it is related to complex sets of variables such as composition, sequence length, hydrophobic characterization, hydrophilic characterization, the sequence order in the fused proteins, linkers and trailer constructs. In this paper, two unique quantitative models are presented for the prediction of the Tt of a family of ELPs that could be fused to different proteins, linkers, and trailers. The lack of need to use multiple software, peptide information, such as PDB file, as well as knowing the second and third structures of proteins are the advantages of this model besides its high accuracy and speed. One of our models could predict the Tt values of the fused ELPs by entering the protein, linker, and trailer features with R2=99%. Also, another model is able to predict the Tt value by entering the fused protein feature with R2=96%. For more reliability, our method is enriched by Artificial Intelligence (AI) to generate similar proteins. In this regard, Generative Adversarial Network (GAN) is our AI method to create fake proteins and similar values. The experimental results show that our strategy for prediction of Tt is reliable in large data.

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An Adversarial and Densely Dilated Network for Connectomes Segmentation

Symmetry ◽

10.3390/sym10100467 ◽

2018 ◽

Vol 10 (10) ◽

pp. 467 ◽

Cited By ~ 2

Author(s):

Ke Chen ◽

Dandan Zhu ◽

Jianwei Lu ◽

Ye Luo

Keyword(s):

State Of The Art ◽

Piriform Cortex ◽

Receptive Fields ◽

Ground Truth ◽

Training Data ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Boundary Map ◽

Segmentation Task ◽

The Brain

Automatic reconstructing of neural circuits in the brain is one of the most crucial studies in neuroscience. Connectomes segmentation plays an important role in reconstruction from electron microscopy (EM) images; however, it is rather challenging due to highly anisotropic shapes with inferior quality and various thickness. In our paper, we propose a novel connectomes segmentation framework called adversarial and densely dilated network (ADDN) to address these issues. ADDN is based on the conditional Generative Adversarial Network (cGAN) structure which is the latest advance in machine learning with power to generate images similar to the ground truth especially when the training data is limited. Specifically, we design densely dilated network (DDN) as the segmentor to allow a deeper architecture and larger receptive fields for more accurate segmentation. Discriminator is trained to distinguish generated segmentation from manual segmentation. During training, such adversarial loss function is optimized together with dice loss. Extensive experimental results demonstrate that our ADDN is effective for such connectomes segmentation task, helping to retrieve more accurate segmentation and attenuate the blurry effects of generated boundary map. Our method obtains state-of-the-art performance while requiring less computation on ISBI 2012 EM dataset and mouse piriform cortex dataset.

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De Novo Generation of Hit-like Molecules from Gene Expression Signatures Using Artificial Intelligence

10.26434/chemrxiv.7294388.v1 ◽

2018 ◽

Cited By ~ 4

Author(s):

Oscar Mendez-Lucio ◽

Benoit Baillif ◽

Djork-Arné Clevert ◽

David Rouquié ◽

Joerg Wichard

Keyword(s):

Gene Expression ◽

Artificial Intelligence ◽

De Novo ◽

Molecular Design ◽

Gene Expression Signature ◽

Cellular Level ◽

Generative Models ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Gene Expression Signatures

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular <i>de novo</i> design and compound optimization. Herein, we report the first generative model that bridges systems biology and molecular design conditioning a generative adversarial network with transcriptomic data. By doing this we could generate molecules that have high probability to produce a desired biological effect at cellular level. We show that this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds as long as the gene expression signature of the desired state is provided. The molecules generated by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures, which is the state-of-the-art method for navigating compound-induced gene expression data. Overall, this method represents a novel way to bridge chemistry and biology to advance in the long and difficult road of drug discovery.

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