Human-in-the-Loop Enhanced COVID-19 Detection in Transfer Learning-Based CNN Models

Author(s):  
Ferhat Ozgur Catak ◽  
Kevser Şahinbaş
2021 ◽  
Author(s):  
Brendon R Lutnick ◽  
Pinaki Sarder

Segmentation of histology tissue whole side images is an important step for tissue analysis. Given enough annotated training data modern neural networks are capable accurate reproducible segmentation, however, the annotation of training datasets is time consuming. Techniques such as human in the loop annotation attempt to reduce this annotation burden, but still require a large amount of initial annotation. Semi-supervised learning, a technique which leverages both labeled and unlabeled data to learn features has shown promise for easing the burden of annotation. Towards this goal, we employ a recently published semi-supervised method: datasetGAN for the segmentation of glomeruli from renal biopsy images. We compare the performance of models trained using datasetGAN and traditional annotation and show that datasetGAN significantly reduces the amount of annotation required to develop a highly performing segmentation model. We also explore the usefulness of using datasetGAN for transfer learning and find that this greatly enhances the performance when a limited number of whole slide images are used for training.


2014 ◽  
Vol 22 (2) ◽  
pp. 179-193
Author(s):  
Nancy Bienert ◽  
Joey Mercer ◽  
Jeffrey R. Homola ◽  
Susan E. Morey ◽  
Thomas Prevot

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>


2014 ◽  
Author(s):  
Hiroshi Kanayama ◽  
Youngja Park ◽  
Yuta Tsuboi ◽  
Dongmook Yi
Keyword(s):  

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.


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