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#1

 
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Deep-learning based classification distinguishes sarcomatoid malignant mesotheliomas from benign spindle cell mesothelial proliferations | Modern Pathology
Description Sarcomatoid mesothelioma is an aggressive malignancy that can be challenging to distinguish from benign spindle cell mesothelial proliferations based on biopsy, and this distinction is crucial to patient treatment and prognosis. A novel deep learning based classifier may be able to aid pathologists in making this critical diagnostic distinction. SpindleMesoNET was trained on cases of malignant sarcomatoid mesothelioma and benign spindle cell mesothelial proliferations. Performance was assessed through cross-validation on the training set, on an independent set of challenging cases referred for expert opinion (‘referral’ test set), and on an externally stained set from outside institutions (‘externally stained’ test set). SpindleMesoNET predicted the benign or malignant status of cases with AUC’s of 0.932, 0.925, and 0.989 on the cross-validation, referral and external test sets, respectively. The accuracy of SpindleMesoNET on the referral set cases (92.5%) was comparable to the average accuracy of 3 experienced pathologists on the same slide set (91.7%). We conclude that SpindleMesoNET can accurately distinguish sarcomatoid mesothelioma from benign spindle cell mesothelial proliferations. A deep learning system of this type holds potential for future use as an ancillary test in diagnostic pathology.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 10 June 2021

#2

 
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Deep learning-based estimation of Flory–Huggins parameter of A–B block copolymers from cross-sectional images of phase-separated structures | Scientific Reports
Description In this study, deep learning (DL)-based estimation of the Flory–Huggins χ parameter of A-B diblock copolymers from two-dimensional cross-sectional images of three-dimensional (3D) phase-separated structures were investigated. 3D structures with random networks of phase-separated domains were generated from real-space self-consistent field simulations in the 25–40 χN range for chain lengths (N) of 20 and 40. To confirm that the prepared data can be discriminated using DL, image classification was performed using the VGG-16 network. We comprehensively investigated the performances of the learned networks in the regression problem. The generalization ability was evaluated from independent images with the unlearned χN. We found that, except for large χN values, the standard deviation values were approximately 0.1 and 0.5 for A-component fractions of 0.2 and 0.35, respectively. The images for larger χN values were more difficult to distinguish. In addition, the learning performances for the 4-class problem were comparable to those for the 8-class problem, except when the χN values were large. This information is useful for the analysis of real experimental image data, where the variation of samples is limited.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 10 June 2021

#3

 
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Google’s AI approach to microchips is welcome — but needs care
Description TBD

#Artificial intelligence
Field # Artificial intelligence
Updated 09 June 2021

#4

 
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Back to the future with machine learning | Nature Machine Intelligence
Description Drug repurposing provides a way to identify effective treatments more quickly and economically. To speed up the search for antiviral treatment of COVID-19, a new platform provides a range of computational models to identify drugs with potential anti-COVID-19 effects.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 09 June 2021

#5

 
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Personalized machine learning of depressed mood using wearables | Translational Psychiatry
Description Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use a combination of longitudinal ecological momentary assessments of depression, neurocognitive sampling synchronized with electroencephalography, and lifestyle data from wearables to generate individualized predictions of depressed mood over a 1-month time period. This study, thus, develops a systematic pipeline for N-of-1 personalized modeling of depression using multiple modalities of data. In the models, we integrate seven types of supervised machine learning (ML) approaches for each individual, including ensemble learning and regression-based methods. All models were verified using fourfold nested cross-validation. The best-fit as benchmarked by the lowest mean absolute percentage error, was obtained by a different type of ML model for each individual, demonstrating that there is no one-size-fits-all strategy. The voting regressor, which is a composite strategy across ML models, was best performing on-average across subjects. However, the individually selected best-fit models still showed significantly less error than the voting regressor performance across subjects. For each individual’s best-fit personalized model, we further extracted top-feature predictors using Shapley statistics. Shapley values revealed distinct feature determinants of depression over time for each person ranging from co-morbid anxiety, to physical exercise, diet, momentary stress and breathing performance, sleep times, and neurocognition. In future, these personalized features can serve as targets for a personalized ML-guided, multimodal treatment strategy for depression.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 09 June 2021

#6

 
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Google is using AI to design processors that run AI more efficiently | New Scientist
Description Google creates custom processors to run its various artificial intelligence algorithms, and now it has tasked an AI with speeding up the process of designing more efficient chips

#Artificial intelligence
Field # Artificial intelligence
Updated 09 June 2021

#7

 
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Engineers apply physics-informed machine learning to solar cell production
Description Today, solar energy provides 2% of U.S. power. However, by 2050, renewables are predicted to be the most used energy source (surpassing petroleum and other liquids, natural gas, and coal) and solar will overtake wind as the ...

#Artificial Intelligence
Field # Artificial Intelligence
Updated 09 June 2021

#8

 
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Humans are ready to take advantage of benevolent AI
Description Humans expect that AI is benevolent and trustworthy. A new study reveals that at the same time humans are unwilling to cooperate and compromise with machines. They even exploit them.

#Artificial intelligence
Field # Artificial intelligence
Updated 08 June 2021

#9

 
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Faster holographic imaging using recurrent neural networks
Description Digital holographic imaging is a commonly used microscopy technique in biomedical imaging. It reveals rich optical information of the sample, which could be used, for example, to detect pathological abnormalities in tissue ...

#Artificial intelligence
Field # Artificial intelligence
Updated 08 June 2021

#10

 
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Artificial intelligence can help you understand music better
Description Algorithms and technology have so far helped listeners to more of the same music. Now, UiO researchers are working on new technology that can get people interested in a greater musical variety.

#Artificial Intelligence
Field # Artificial Intelligence
Updated 08 June 2021

#11

 
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Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids | Scientific Reports
Description Reliable and accurate prediction of complex fluids’ response under flow is of great interest across many disciplines, from biological systems to virtually all soft materials. The challenge is to solve non-trivial time and rate dependent constitutive equations to describe these structured fluids under various flow protocols. We present Rheology-Informed Neural Networks (RhINNs) for solving systems of Ordinary Differential Equations (ODEs) adopted for complex fluids. The proposed RhINNs are employed to solve the constitutive models with multiple ODEs by benefiting from Automatic Differentiation in neural networks. In a direct solution, the RhINNs platform accurately predicts the fully resolved solution of constitutive equations for a Thixotropic-Elasto-Visco-Plastic (TEVP) complex fluid for a series of flow protocols. From a practical perspective, an exhaustive list of experiments are required to identify model parameters for a multi-variant constitutive TEVP model. RhINNs are found to learn these non-trivial model parameters for a complex material using a single flow protocol, enabling accurate modeling with limited number of experiments and at an unprecedented rate. We also show the RhINNs are not limited to a specific model and can be extended to include various models and recover complex manifestations of kinematic heterogeneities and transient shear banding of thixotropic fluids.

#Artificial intelligence
Field # Artificial intelligence
Updated 08 June 2021

#12

 
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Army researchers develop innovative framework for training AI
Description Army researchers have developed a pioneering framework that provides a baseline for the development of collaborative multi-agent systems.

#Artificial intelligence
Field # Artificial intelligence
Updated 07 June 2021

#13

 
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DeepMind AI taught digital people to play football from scratch | New Scientist
Description AI taught digital players who start out able to only make random movements how to run, dribble a football, kick it into the goal and work as a team

#Artificial intelligence
Field # Artificial intelligence
Updated 07 June 2021

#14

 
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Artificial Intelligence Helps Design Better Antibody Drugs
Description TBD

#Artificial Intelligence
Field # Artificial Intelligence
Updated 06 June 2021

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