Intra-class consistency and inter-class discrimination feature learning for automatic skin lesion classification

Wang, Lituan; Zhang, Lei; Shu, Xin; Zhang, Yi

doi:10.1016/j.media.2023.102746

Cited by 20 publications

(3 citation statements)

References 31 publications

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“…The experimental findings indicate that the streamlined MobileNetV2 model can get a classification accuracy of up to 85% for 8 distinct skin conditions, while simultaneously minimizing parameters and computational demands. Wang L, Zhang L, Shu X, et al [19] introduce a deep learning methodology designed to improve both the consistency within a certain class and the ability to distinguish between different classes in the automatic classification of skin lesions. The approach proposed by the researchers has strong generalizability and has the capacity to dynamically highlight more distinct locations within the skin lesion.…”

Section: Related Work 21 Deep Learning For Diagnosis Task Of Skin Cancermentioning

confidence: 99%

Adversarial selective domain adaptation with feature cluster for skin cancer diagnosis

Gou,

Cui

2024

Preprint

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Medical imaging approaches widely employ deep neural networks for the investigation and diagnosis of different skin disorders. However, recent studies suggest that even a proficient model based on deep learning might struggle with generalization when applied to datasets from disparate cohorts due to domain shift phenomena. Meanwhile, there are usually need many well-labelled images utilized for the training process to attain a stronger level of performance. In order to alleviate the domain shift and the necessity for adequate training data, we introduce a novel method termed as adversarial selective domain adaption with feature cluster (ASDA). It achieves effective performance improvement of model when the target dataset is smaller than the source dataset. Specifically, we generate a set of feature clusters for each sample in the target domain to alleviate the demand for data. Subsequently, a conditional domain adversarial network is used to mitigate domain shift. Finally, due to consistency issues between feature clusters and samples, we propose a method of selective minmax entropy to maintain consistency. Our method diverges from typical domain adaption approaches that solely target reducing the domain gap. Instead, we address both the discrepancy between domains and the problem of limited data in the target dataset simultaneously. Extensive experiments have been undertaken on datasets pertaining to skin cancer, that confirms ASDA's efficacy in skin cancer diagnosis for dermatoscopic and clinic image.

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Section: Related Work 21 Deep Learning For Diagnosis Task Of Skin Cancermentioning

confidence: 99%

Adversarial selective domain adaptation with feature cluster for skin cancer diagnosis

Gou,

Cui

2024

Preprint

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“…ResNet first introduced residual connections in DNNs, which addresses issues such as gradient vanishing and gradient explosion encountered in DNNs. This enables the training of very deep neural without compromising performance and has demenstraed excellent performance on various computer vision tasks, including skin lesion classification [65], [66], [67], [68]. Instead of training ResNet50 from scratch, we utilize transfer learning and initialize ResNet50 with ImageNet pre-trained weights (T-ResNet50).…”

Section: Stgan-based Skin Lesion Classification Frameworkmentioning

confidence: 99%

A GAN-Based Data Augmentation Method for Imbalanced Multi-Class Skin Lesion Classification

Su,

Hamed,

Isa

et al. 2024

IEEE Access

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Skin cancer is one of the most common types of cancer globally. Despite the remarkable advancements of deep learning methods in computer vision, automatic diagnosis of skin diseases still faces challenges such as limited data and class imbalance. Generative Adversarial Networks (GANs), which can synthesize realistic data, appear as an alternative to mitigate these issues. However, for imbalanced data, unconditional GANs either generate uneven data distribution or neglect universal knowledge of the whole dataset, while state-of-the-art (SOTA) conditional GANs suffer from performance degradation due to the mode collapse of minority classes. This paper proposes a two-stage GAN-based method to synthesize finegrained and diverse 256×256 pixels skin lesion images for the imbalanced dataset, named Self-Transfer GAN (STGAN). STGAN first learns universal knowledge from all classes then transfers this shared knowledge to each class and fuses it with class-specific knowledge to synthesize high-quality images. Furthermore, based on STGAN, a framework to enhance the classification performance is established. Both data generation and classification tasks are evaluated on HAM10000 dataset. In terms of Fré chet Inception Distance (FID), Inception Score, Precision, and Recall, STGAN improved by 16%, 16%, 4%, 33% compared with SOTA conditional StyleGAN2. For classification, the STGAN-based framework achieves remarkable results, with an Accuracy of 98.23%, Sensitivity of 88.85%, Precision of 90.23%, F1-score of 89.48%, and Specificity of 98.34%.

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“…Each subject's brain has individual heterogeneity. Suppressing individual heterogeneity can reveal commonalities of diseases, which further helps researchers and physicians understand the mechanisms involved and diagnose diseases [179,180]. There are two directions which may be useful to suppress individual heterogeneity in GNN models:…”

Section: Individual Heterogeneitymentioning

confidence: 99%

The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook

Zhang,

Yang,

Zhang

et al. 2023

Brain Sciences

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Neurological disorders (NDs), such as Alzheimer’s disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects, thus becoming one of the best deep learning models in the diagnosis of ND. Some researchers have investigated the application of GNN in the medical field, but the scope is broad, and its application to NDs is less frequent and not detailed enough. This review focuses on the research progress of GNNs in the diagnosis of ND. Firstly, we systematically investigated the GNN framework of ND, including graph construction, graph convolution, graph pooling, and graph prediction. Secondly, we investigated common NDs using the GNN diagnostic model in terms of data modality, number of subjects, and diagnostic accuracy. Thirdly, we discussed some research challenges and future research directions. The results of this review may be a valuable contribution to the ongoing intersection of artificial intelligence technology and brain imaging.

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Intra-class consistency and inter-class discrimination feature learning for automatic skin lesion classification

Cited by 20 publications

References 31 publications

Adversarial selective domain adaptation with feature cluster for skin cancer diagnosis

Adversarial selective domain adaptation with feature cluster for skin cancer diagnosis

A GAN-Based Data Augmentation Method for Imbalanced Multi-Class Skin Lesion Classification

The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook

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