Predicting treatment outcome of intracranial aneurysms using angiographic parametric imaging and recurrent neural networks

2020 ◽  
Author(s):  
Mohammad Mahdi Shiraz Bhurwani ◽  
Mohammad Waqas ◽  
Kyle A. Williams ◽  
Ryan A. Rava ◽  
Alexander R. Podgorsak ◽  
...  
Keyword(s):  
Neural Networks ◽  
Treatment Outcome ◽  
Recurrent Neural Networks ◽  
Intracranial Aneurysms ◽  
Parametric Imaging ◽  
Predicting Treatment Outcome

Feasibility study of deep neural networks to classify intracranial aneurysms using angiographic parametric imaging

2019 ◽  
Author(s):  
Mohammad Mahdi Shiraz Bhurwani ◽  
Alexander R. Podgorsak ◽  
Anusha Ramesh Chandra ◽  
Ryan A. Rava ◽  
Kenneth V. Snyder ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feasibility Study ◽  
Intracranial Aneurysms ◽  
Deep Neural Networks ◽  
Parametric Imaging

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Mathematical model for predicting treatment outcome in patients with destructive pulmonary multiresistant tuberculosis

2020 ◽  
Vol 0 (4) ◽  
pp. 24-32
Author(s):  
O.S. Shevchenko ◽  
L.D. Todoriko ◽  
I.A. Ovcharenko ◽  
Y.B. Radzishevskа ◽  
S.S. Ovcharenko
Keyword(s):  
Mathematical Model ◽  
Treatment Outcome ◽  
Predicting Treatment Outcome
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