Evaluating the Impact of Training Loss on MR to Synthetic CT Conversion

Sherwani, Moiz Khan; Zaffino, Paolo; Bruno, Pierangela; Spadea, Maria Francesca; Calimeri, Francesco

doi:10.1007/978-3-030-64583-0_50

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Additionally, the proposed method can be adopted in smartphone devices. 28,29 Additionally, the proposed deep neural network can be used for other real-time applications such as vehicle classification. [30][31][32]…”

Section: Discussionmentioning

confidence: 99%

Movie Genre Classification from RGB Movie Poster Image Using Deep Feed-forward Network

Korai

Bouk

Sindhi

2021

Yanbu Journal of Engineering and Science

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A movie poster image is one of the important media in the filmmaking process, providing valuable information about the movie, such as movie titles, characters, and genres. Identifying a movie genre from a poster can be a daunting task, as it can relate to multiple genres. To solve this problem, this paper uses a deep feedforward neural network to classify movie genres from movie poster images. In this regard, we used and trained a state-of-the-art InceptionV3 deep neural network. The network is trained on our dataset consisting of 36,423 movie poster images taken from the IMDB website, which is categorized into 28 genres. The model predicts the top three classes with the highest probability of a particular movie poster.

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Section: Discussionmentioning

confidence: 99%

Movie Genre Classification from RGB Movie Poster Image Using Deep Feed-forward Network

Korai

Bouk

Sindhi

2021

Yanbu Journal of Engineering and Science

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“…The most recent work presented for the COVID-19 lesion segment using U-Net++ was proposed by Zhou et al [17]. Most recent work focuses on image translation, segmentation [18], and generation of synthetic images [19] using supervised and semi-supervised learning to overcome this issue and to train the network for unsupervised segmentation, GAN, Cycle-GAN [14], and variational auto-encoder [20] are used. Other unsupervised approaches using similar generative models for image-to-image translation are DualGAN [21] and UNIT [22].…”

Section: Related Workmentioning

confidence: 99%

Lesion segmentation in lung CT scans using unsupervised adversarial learning

Sherwani

Marzullo

Momi

et al. 2022

Med Biol Eng Comput

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Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack of annotated data, which is common in medical imaging, is an issue; in this context, interesting approaches can use unsupervised learning to accurately distinguish between healthy tissues and lesions, training the network without using the annotations. In this work, an unsupervised learning technique is proposed to automatically segment coronavirus disease 2019 (COVID-19) lesions on 2D axial CT lung slices. The proposed approach uses the technique of image translation to generate healthy lung images based on the infected lung image without the need for lesion annotations. Attention masks are used to improve the quality of the segmentation further. Experiments showed the capability of the proposed approaches to segment the lesions, and it outperforms a range of unsupervised lesion detection approaches. The average reported results for the test dataset based on the metrics: Dice Score, Sensitivity, Specificity, Structure Measure, Enhanced-Alignment Measure, and Mean Absolute Error are 0.695, 0.694, 0.961, 0.791, 0.875, and 0.082 respectively. The achieved results are promising compared with the state-of-the-art and could constitute a valuable tool for future developments. Graphical abstract

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