Xray-Net: Self-supervised pixel stretching approach to improve low-contrast medical imaging

Mouzai, Meriem; Mustapha, Aouache; Bousmina, Zaid; Keskas, Ilyes; Farhi, Faiza

doi:10.1016/j.compeleceng.2023.108859

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…• Following this, a common vector of batch data may be obtained by subtracting the a b,diff from the reference vector (a t ), as seen in Eq. (13).…”

Section: Using Cva As Pooling Layermentioning

confidence: 99%

“…For improving dental disease diagnosis and treatment plans, deep learning [7] has significantly influenced the processing of intraoral X-ray images in image processing [8], segmentation [9][10][11][12] and enhancements [13][14][15]. These developments integrating intraoral X-ray imaging with deep learning techniques enhance the precision of oral health condition recognition and detection, such as dental caries [16][17][18][19][20][21][22], implant [23], oriented tooth [24], restoration by filling [25] and dental material [26].…”

Section: Introductionmentioning

confidence: 99%

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CVApool: using null-space of CNN weights for the tooth disease classification

Can,

Isik,

Anagun

2024

Neural Comput & Applic

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In light of current developments in dental care, dental professionals have increasingly used deep learning methods to get precise diagnoses of oral problems. Using intraoral X-rays in dental radiography is imperative in many dental interventions. Integrating deep learning techniques with a unique collection of intraoral X-ray images has been undertaken to enhance the accuracy of dental disease detection. In this study, we propose an alternative pooling layer, namely the Common Vector Approach Pooling technique, to address the constraints associated with average pooling in deep learning methods. The experiments are conducted on a large dataset, involving twenty different dental conditions, divided into seven categories. Our proposed approach achieved a high accuracy rate of 86.4% in identifying dental problems across the seven oral categories.

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“…• Following this, a common vector of batch data may be obtained by subtracting the a b,diff from the reference vector (a t ), as seen in Eq. (13).…”

Section: Using Cva As Pooling Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CVApool: using null-space of CNN weights for the tooth disease classification

Can,

Isik,

Anagun

2024

Neural Comput & Applic

View full text Add to dashboard Cite

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Colorizing Multi-Modal Medical Data: An Autoencoder-based Approach for Enhanced Anatomical Information in X-ray Images

Saini,

Venkatesh,

Ganesh

et al. 2024

EAI Endorsed Trans Perv Health Tech

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Colourisation is the process of synthesising colours in black and white images without altering the image’s structural content and semantics. The authors explore the concept of colourisation, aiming to colourise the multi-modal medical data through X-rays. Colourized X-ray images have a better potential to portray anatomical information than their conventional monochromatic counterparts. These images contain precious anatomical information that, when colourised, will become very valuable and potentially display more information for clinical diagnosis. This will help improve understanding of these X-rays and significantly contribute to the arena of medical image analysis. The authors have implemented three models, a basic auto-encoder architecture, and two combined learnings of the autoencoder module with transfer learning of pre-trained neural networks. The unique feature of this proposed framework is that it can colourise any medical modality in the medical imaging domain. The framework’s performance is evaluated on a chest x-ray image dataset, and it has produced benchmark results enabling high-quality colourisation. The biggest challenge is the need for a correct solution for the mapping between intensity and colour. This makes human interaction and external information from medical professionals crucial for interpreting the results.

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Xray-Net: Self-supervised pixel stretching approach to improve low-contrast medical imaging

Cited by 2 publications

References 20 publications

CVApool: using null-space of CNN weights for the tooth disease classification

CVApool: using null-space of CNN weights for the tooth disease classification

Colorizing Multi-Modal Medical Data: An Autoencoder-based Approach for Enhanced Anatomical Information in X-ray Images

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