Deep Convolutional Generative Adversarial Networks to Enhance Artificial Intelligence in Healthcare: A Skin Cancer Application

Salvia, Marco La; Torti, Emanuele; Leon, Raquel; Fabelo, Himar; Ortega, Sebastián; Martinez-Vega, Beatriz; Leporati, Francesco

doi:10.3390/s22166145

Cited by 17 publications

(9 citation statements)

References 35 publications

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“…69 As a separate part of the HELICoiD project, an in vitro histology dataset was also produced 70 and employed for classification, achieving high-accuracy results with classical and later deep learning methods, 40,71,72 including using superpixel aggregation. 73 The experience gathered by the HELICoiD group also led to the application of similar methods for the classification of skin cancers, 74,75 Alzheimer's disease, 76 gastroenterology, 29 thyroid 42 and ENT cancers. 41,43,77 An independent brain cancer dataset containing 13 images of 12 patients was collected and analyzed by Ref.…”

Section: Hsi In Brain Cancer Surgerymentioning

confidence: 99%

Intra-operative brain tumor detection with deep learning-optimized hyperspectral imaging

Giannantonio¹,

Alperovich

Piercosimo

et al. 2023

Optical Biopsy XXI: Toward Real-Time Spectroscopic Imaging and Diagnosis

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Surgery for gliomas (intrinsic brain tumors), especially when low-grade, is challenging due to the infiltrative nature of the lesion. Currently, no real-time, intra-operative, label-free and wide-field tool is available to assist and guide the surgeon to find the relevant demarcations for these tumors. While marker-based methods exist for the high-grade glioma case, there is no convenient solution available for the low-grade case; thus, marker-free optical techniques represent an attractive option. Although RGB imaging is a standard tool in surgical microscopes, it does not contain sufficient information for tissue differentiation. We leverage the richer information from hyperspectral imaging (HSI), acquired with a snapscan camera in the 468 − 787 nm range, coupled to a surgical microscope, to build a deep-learning-based diagnostic tool for cancer resection with potential for intra-operative guidance. However, the main limitation of the HSI snapscan camera is the image acquisition time, limiting its widespread deployment in the operation theater. Here, we investigate the effect of HSI channel reduction and pre-selection to scope the design space for the development of cheaper and faster sensors. Neural networks are used to identify the most important spectral channels for tumor tissue differentiation, optimizing the trade-off between the number of channels and precision to enable real-time intra-surgical application. We evaluate the performance of our method on a clinical dataset that was acquired during surgery on five patients. By demonstrating the possibility to efficiently detect low-grade glioma, these results can lead to better cancer resection demarcations, potentially improving treatment effectiveness and patient outcome.

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Section: Hsi In Brain Cancer Surgerymentioning

confidence: 99%

Intra-operative brain tumor detection with deep learning-optimized hyperspectral imaging

Giannantonio¹,

Alperovich

Piercosimo

et al. 2023

Optical Biopsy XXI: Toward Real-Time Spectroscopic Imaging and Diagnosis

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“…Machine learning algorithms, such as deep learning, have been effectively employed for feature extraction and classification recognition of hyperspectral images (16,80). Moreover, the research field has continuously expanded its scope to encompass the classification and recognition of various other subjects including cancer, tumor, and skin (64,(81)(82)(83), forming a positive development trend that keeps pace with the times.…”

Section: Research Statusmentioning

confidence: 99%

Bibliometric analysis of the current status and trends on medical hyperspectral imaging

Jiang,

Ma,

Tan

et al. 2023

Front. Med.

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Hyperspectral imaging (HSI) is a promising technology that can provide valuable support for the advancement of the medical field. Bibliometrics can analyze a vast number of publications on both macroscopic and microscopic levels, providing scholars with essential foundations to shape future directions. The purpose of this study is to comprehensively review the existing literature on medical hyperspectral imaging (MHSI). Based on the Web of Science (WOS) database, this study systematically combs through literature using bibliometric methods and visualization software such as VOSviewer and CiteSpace to draw scientific conclusions. The analysis yielded 2,274 articles from 73 countries/regions, involving 7,401 authors, 2,037 institutions, 1,038 journals/conferences, and a total of 7,522 keywords. The field of MHSI is currently in a positive stage of development and has conducted extensive research worldwide. This research encompasses not only HSI technology but also its application to diverse medical research subjects, such as skin, cancer, tumors, etc., covering a wide range of hardware constructions and software algorithms. In addition to advancements in hardware, the future should focus on the development of algorithm standards for specific medical research targets and cultivate medical professionals of managing vast amounts of technical information.

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“…DCGAN was trained with 5000 RGB images (50x50x3 resolution) from HAM10000 then hyperspectral images (acquired from two hospitals) were used to apply transfer learning on the GAN model (50x50x116 resolution). Resnet18 was trained on synthetic data only and scored an accuracy above 80% [ 10 ]. In this paper we propose using generative adversarial networks to give the ability to the medical community to generate datasets of realistic artificial faces with acne disease and to remove the barriers on access to data by generating highly realistic synthetic images that can allow us to reach same results as when using real images.…”

Section: Introductionmentioning

confidence: 99%

Generative adversarial networks for anonymous acneic face dataset generation

Zein,

Chantaf,

Fournier

et al. 2024

PLoS ONE

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It is well known that the performance of any classification model is effective if the dataset used for the training process and the test process satisfy some specific requirements. In other words, the more the dataset size is large, balanced, and representative, the more one can trust the proposed model’s effectiveness and, consequently, the obtained results. Unfortunately, large-size anonymous datasets are generally not publicly available in biomedical applications, especially those dealing with pathological human face images. This concern makes using deep-learning-based approaches challenging to deploy and difficult to reproduce or verify some published results. In this paper, we propose an efficient method to generate a realistic anonymous synthetic dataset of human faces, focusing on attributes related to acne disorders at three distinct levels of severity (Mild, Moderate, and Severe). Notably, our approach initiates from a small dataset of facial acne images, leveraging generative techniques to augment and diversify the dataset, ensuring comprehensive coverage of acne severity levels while maintaining anonymity and realism in the synthetic data. Therefore, a specific hierarchy StyleGAN-based algorithm trained at distinct levels is considered. Moreover, the utilization of generative adversarial networks for augmentation offers a means to circumvent potential privacy or legal concerns associated with acquiring medical datasets. This is attributed to the synthetic nature of the generated data, where no actual subjects are present, thereby ensuring compliance with privacy regulations and legal considerations. To evaluate the performance of the proposed scheme, we consider a CNN-based classification system, trained using the generated synthetic acneic face images and tested using authentic face images. Consequently, we show that an accuracy of 97.6% is achieved using InceptionResNetv2. As a result, this work allows the scientific community to employ the generated synthetic dataset for any data processing application without restrictions on legal or ethical concerns. Moreover, this approach can also be extended to other applications requiring the generation of synthetic medical images.

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Deep Convolutional Generative Adversarial Networks to Enhance Artificial Intelligence in Healthcare: A Skin Cancer Application

Cited by 17 publications

References 35 publications

Intra-operative brain tumor detection with deep learning-optimized hyperspectral imaging

Intra-operative brain tumor detection with deep learning-optimized hyperspectral imaging

Bibliometric analysis of the current status and trends on medical hyperspectral imaging

Generative adversarial networks for anonymous acneic face dataset generation

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