Visualizing CoAtNet Predictions for Aiding Melanoma Detection

Kvak, Daniel

doi:10.20944/preprints202205.0302.v1

Cited by 3 publications

(3 citation statements)

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“…This involves training the model with sample data, evaluating performance by assessing the loss functions on a validation data set, and ultimately gauging model effectiveness on a testing dataset. Swin, an acronym for Shifted window (SwinT) [30], generates hierarchical feature map by integrating image patches in deep layer. ViT, a novel transformer-based framework, incorporates spatial dimension conversion within its structure.…”

Section: Model Trainingmentioning

confidence: 99%

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HLASwin-T-ACoat-Net Based Underwater Object Detection

Manimurugan,

Narmatha,

Aborokbah

et al. 2024

IEEE Access

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Due to the limited light penetration in underwater environments, sonar equipment plays a crucial role in various commercial and military operations. However, underwater images often suffer from degradation due to scattering and absorption phenomena, resulting in poor visibility of submerged objects. To address this challenge, image enhancement techniques are essential for enhancing the appearance and visibility of underwater objects. This research proposes a novel approach called HLAST-ACNet, which combines the advantages of a hybrid Local Acuity Swin Transformer and an Adapted Coat-Net for Underwater Object Detection (UOD). The HLASwin-T-ACoat-Net leverages Contrast Limited Adaptive Histogram Equalization (CLAHE) to increase the quality of images. Additionally, it incorporates a path aggregation network to integrate deep and shallow feature maps and utilizes online complicated example mining to improve training efficiency. Furthermore, the algorithm improves Region of Interest (ROI) pooling by introducing ROI alignment, which mitigates quantization errors and enhances object detection accuracy. Compared to existing algorithms, the algorithms based on HLASTACNet demonstrate significant improvements in the URPC2018 and OUC datasets, achieving precision rates of 91.25% and 92.36%, respectively. The research model has a higher computational complexity than four existing methods, as evidenced by its GFLOPs, per-image processing time with a speed of 20ms, and the FPS measures for average processed frames per second reaching 2.28s. The research model effectively addressed the challenges and false detection with varying sizes of objects in complicated underwater environments.

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Section: Model Trainingmentioning

confidence: 99%

“…This is employed as a means of depicting the visual representation of the models' forecasts. Firstly, the gradient of the score 𝑔 𝒸 is computed concerning the feature maps (𝑓 𝑘 ) of a specific layer according to the formula presented in [30]. This calculation is performed before the SoftMax operation.…”

Section: Roi Poolingmentioning

confidence: 99%

HLASwin-T-ACoat-Net Based Underwater Object Detection

Manimurugan,

Narmatha,

Aborokbah

et al. 2024

IEEE Access

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“…[160] demonstrated that CoAtNet was effective in classifying renal parenchymal tumor subtypes and performed better than ViT. Kvak [161] used CoAtNet to classify malignant and benign melanoma and obtained an accuracy of 0.901, which is better than the accuracies obtained with other state‐of‐the‐art algorithms. Tripathi et al.…”

Section: Predictive Models or Machine Learning Algorithms For Classif...mentioning

confidence: 99%

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

Lei

et al. 2023

iRADIOLOGY

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The liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.

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Parasitic egg recognition using convolution and attention network

AlDahoul,

Karim,

Momo

et al. 2023

Sci Rep

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Intestinal parasitic infections (IPIs) caused by protozoan and helminth parasites are among the most common infections in humans in low-and-middle-income countries. IPIs affect not only the health status of a country, but also the economic sector. Over the last decade, pattern recognition and image processing techniques have been developed to automatically identify parasitic eggs in microscopic images. Existing identification techniques are still suffering from diagnosis errors and low sensitivity. Therefore, more accurate and faster solution is still required to recognize parasitic eggs and classify them into several categories. A novel Chula-ParasiteEgg dataset including 11,000 microscopic images proposed in ICIP2022 was utilized to train various methods such as convolutional neural network (CNN) based models and convolution and attention (CoAtNet) based models. The experiments conducted show high recognition performance of the proposed CoAtNet that was tuned with microscopic images of parasitic eggs. The CoAtNet produced an average accuracy of 93%, and an average F1 score of 93%. The finding opens door to integrate the proposed solution in automated parasitological diagnosis.

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Visualizing CoAtNet Predictions for Aiding Melanoma Detection

Cited by 3 publications

References 0 publications

HLASwin-T-ACoat-Net Based Underwater Object Detection

HLASwin-T-ACoat-Net Based Underwater Object Detection

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

Parasitic egg recognition using convolution and attention network

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