BreastScreening

Calisto, Francisco Maria; Nunes, Nuno; Nascimento, Jacinto C.

doi:10.1145/3399715.3399744

Cited by 36 publications

(5 citation statements)

References 20 publications

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“…Alkan et al [24] provide various means and measures to enhance the interactivity of AI solutions and strategies for resilient models in healthcare, such as human-centered computing, human-computer interaction (HCI), and interactive systems and tools. Calisto et al [25] detail the field research, design, and comparative implementation of a multimodal user interface for medical imaging in breast screening. The article concludes by summarizing the findings and offering recommendations from radiologists to guide the future design of medical imaging interfaces.…”

Section: Artificial Intelligence In Healthcarementioning

confidence: 99%

Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants

Tiwari,

Dandabani,

Das

et al. 2023

Diagnostics

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The mortality rates of patients contracting the Omicron and Delta variants of COVID-19 are very high, and COVID-19 is the worst variant of COVID. Hence, our objective is to detect COVID-19 Omicron and Delta variants from lung CT-scan images. We designed a unique ensemble model that combines the CNN architecture of a deep neural network—Capsule Network (CapsNet)—and pre-trained architectures, i.e., VGG-16, DenseNet-121, and Inception-v3, to produce a reliable and robust model for diagnosing Omicron and Delta variant data. Despite the solo model’s remarkable accuracy, it can often be difficult to accept its results. The ensemble model, on the other hand, operates according to the scientific tenet of combining the majority votes of various models. The adoption of the transfer learning model in our work is to benefit from previously learned parameters and lower data-hunger architecture. Likewise, CapsNet performs consistently regardless of positional changes, size changes, and changes in the orientation of the input image. The proposed ensemble model produced an accuracy of 99.93%, an AUC of 0.999 and a precision of 99.9%. Finally, the framework is deployed in a local cloud web application so that the diagnosis of these particular variants can be accomplished remotely.

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Section: Artificial Intelligence In Healthcarementioning

confidence: 99%

Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants

Tiwari,

Dandabani,

Das

et al. 2023

Diagnostics

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“…Deep learning-based segmentation models have significantly enhanced a variety of medical procedures, including brain tumor detection [1,2], breast cancer screening [3,4], organ segmentation [5,6], and skin lesion analysis [2,7,8]. Furthermore, these models contribute to the synchronized monitoring of medical devices and patients, exemplified by the detection of artificial ventilation usage [9].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Adversarial Attacks on Medical Segmentation Model via Transformation of Feature Statistics

Lee,

Ju,

Sim

et al. 2024

Applied Sciences

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Deep learning-based segmentation models have made a profound impact on medical procedures, with U-Net based computed tomography (CT) segmentation models exhibiting remarkable performance. Yet, even with these advances, these models are found to be vulnerable to adversarial attacks, a problem that equally affects automatic CT segmentation models. Conventional adversarial attacks typically rely on adding noise or perturbations, leading to a compromise between the success rate of the attack and its perceptibility. In this study, we challenge this paradigm and introduce a novel generation of adversarial attacks aimed at deceiving both the target segmentation model and medical practitioners. Our approach aims to deceive a target model by altering the texture statistics of an organ while retaining its shape. We employ a real-time style transfer method, known as the texture reformer, which uses adaptive instance normalization (AdaIN) to change the statistics of an image’s feature.To induce transformation, we modify the AdaIN, which typically aligns the source and target image statistics. Through rigorous experiments, we demonstrate the effectiveness of our approach. Our adversarial samples successfully pass as realistic in blind tests conducted with physicians, surpassing the effectiveness of contemporary techniques. This innovative methodology not only offers a robust tool for benchmarking and validating automated CT segmentation systems but also serves as a potent mechanism for data augmentation, thereby enhancing model generalization. This dual capability significantly bolsters advancements in the field of deep learning-based medical and healthcare segmentation models.

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“…Another study investigated two distinct applications utilising deep learning-based generative adversarial networks (GANs) and transfer learning for magnetic resonance imaging (MRI) reconstruction procedures for brain and knee imaging. The approach facilitates the implementation of forthcoming MRI reconstruction models, obviating the need for extensive imaging datasets [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Robust Medical Image Watermarking Scheme Using PSO, LWT, and Hessenberg Decomposition

et al. 2023

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Digital imaging is a technology that is extensively employed in diverse diagnostic examinations such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound imaging, among other modalities. Transferring a patient’s diagnostic images and medical data to a specialist physician in a distinct geographical location is conducted to facilitate an accurate diagnosis. The safeguarding of patient data privacy and confidentiality is ensured through the utilisation of smart hospital applications for medical data security. The current research presents the effective utilisation of lifting wavelet transform (LWT) and Hessenberg-based particle swarm optimization in order to generate resilient and safeguarded watermarks on ultrasound images. The empirical evidence suggests that our innovative approach outperforms our prior methodology, established through extensive testing. The watermark’s imperceptibility and accuracy are exemplified by its capacity to sustain a superior structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), even amidst diverse image processing assaults.

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BreastScreening

Cited by 36 publications

References 20 publications

Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants

Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants

Adversarial Attacks on Medical Segmentation Model via Transformation of Feature Statistics

Robust Medical Image Watermarking Scheme Using PSO, LWT, and Hessenberg Decomposition

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