Transparent Adaptation in Deep Medical Image Diagnosis

Kollias, Dimitrios; Vlaxos, Y.; Seferis, M.; Kollia, Ilianna; Sukissian, Levon; Wingate, James; Kollias, Stefanos

doi:10.1007/978-3-030-73959-1_22

Cited by 39 publications

(19 citation statements)

References 28 publications

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“…Mixup [59] constitutes a simple but powerful data augmentation routine that has already been applied in various tasks in Computer Vision, Natural Language Processing (NLP) and the audio domain. Some indicative examples pertain to medical image segmentation [5], sentence classification [2,10,49], audio tagging [55], audio scene classification [56] and image classification [12,16,17,26,28].…”

Section: Related Workmentioning

confidence: 99%

MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition

Psaroudakis¹,

Kollias²

2022

Preprint

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Automatic Facial Expression Recognition (FER) has attracted increasing attention in the last 20 years since facial expressions play a central role in human communication. Most FER methodologies utilize Deep Neural Networks (DNNs) that are powerful tools when it comes to data analysis. However, despite their power, these networks are prone to overfitting, as they often tend to memorize the training data. What is more, there are not currently a lot of in-the-wild (i.e. in unconstrained environment) large databases for FER. To alleviate this issue, a number of data augmentation techniques have been proposed. Data augmentation is a way to increase the diversity of available data by applying constrained transformations on the original data. One such technique, which has positively contributed to various classification tasks, is Mixup. According to this, a DNN is trained on convex combinations of pairs of examples and their corresponding labels.In this paper, we examine the effectiveness of Mixup for in-the-wild FER in which data have large variations in head poses, illumination conditions, backgrounds and contexts. We then propose a new data augmentation strategy which is based on Mixup, called MixAugment. According to this, the network is trained concurrently on a combination of virtual examples and real examples; all these examples contribute to the overall loss function. We conduct an extensive experimental study that proves the effectiveness of MixAugment over Mixup and various state-of-the-art methods. We further investigate the combination of dropout with Mixup and MixAugment, as well as the combination of other data augmentation techniques with MixAugment.

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Section: Related Workmentioning

confidence: 99%

MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition

Psaroudakis¹,

Kollias²

2022

Preprint

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“…The presented approach is based on a CNN-RNN architecture that performs 3-D CT scan analysis. The method follows our previous work [4,6,5,11] on developing deep neural architectures for predicting COVID-19, as well as neurodegenerative and other [7,5,12,14] diseases and medical situations.…”

Section: Related Workmentioning

confidence: 99%

MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis

Kollias¹,

Arsenos²,

Soukissian³

et al. 2021

Preprint

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Early and reliable COVID-19 diagnosis based on chest 3-D CT scans can assist medical specialists in vital circumstances. Deep learning methodologies constitute a main approach for chest CT scan analysis and disease prediction. However, large annotated databases are necessary for developing deep learning models that are able to provide COVID-19 diagnosis across various medical environments in different countries. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-enabled diagnosis methods of COVID-19 based on CT scans.In this paper we present the COV19-CT-DB database which is annotated for COVID-19, consisting of about 5,000 3-D CT scans, We have split the database in training, validation and test datasets. The former two datasets can be used for training and validation of machine learning models, while the latter will be used for evaluation of the developed models. We also present a deep learning approach, based on a CNN-RNN network and report its performance on the COVID19-CT-DB database.

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“…Deep learning has been used in a lot of medical imaging analysis and diagnosis, e.g. in [13], [12], [14].…”

Section: Related Workmentioning

confidence: 99%

A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images

Tan¹,

Liu²

2021

Preprint

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We present an automatic COVID1-19 diagnosis framework from lung CT-scan slice images. In this framework, the slice images of a CT-scan volume are first proprocessed using segmentation techniques to filter out images of closed lung, and to remove the useless background. Then a resampling method is used to select a set of fixed number of slice images for training and validation. A 3D CNN network with BERT is used to classify this set of selected slice images. In this network, an embedding feature is also extracted. In cases where there are more than one set of slice images in a volume, the features of all sets are extracted and pooled into a feature vector for the whole CT-scan volume. A simple multiple-layer perceptron (MLP) network is to used to further classify the aggregated feature vector. The models are trained and evaluated on the provided training and validation datasets. On the validation dataset, the precision is 0.906 and the F1 score is 0.89.

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Transparent Adaptation in Deep Medical Image Diagnosis

Cited by 39 publications

References 28 publications

MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition

MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition

MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis

A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images

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