Class attention to regions of lesion for imbalanced medical image recognition

Zhuang, Jiaxin; Cai, Jiabin; Zhang, Jianguo; Zheng, Wei‐Shi; Wang, Ruixuan

doi:10.1016/j.neucom.2023.126577

Cited by 7 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies have been conducted significant research in the realm of skin cancer detection and classification, particularly focusing on the challenge of imbalanced datasets in dermatology [89,[95][96][97][98][99][100][101][102][103][104]. These studies collectively advanced the field of medical image classification by addressing the critical issue of imbalanced datasets, each contributing novel techniques and frameworks to improve classification accuracy and efficiency in dermatological and other medical imaging applications.…”

Section: The Challenge Of Imbalanced Datamentioning

confidence: 99%

“…They introduced a new perspective on distribution alignment and a variable condition queue (VCQ) module to maintain a balanced number of unlabeled samples for each class, showing competitive performance in various medical image classification tasks. Zhuang, Cai, Zhang, Zheng and Wang [101] introduced the class attention to regions of the lesion (CARE) framework, which embedded attention into the CNN training process to focus on lesion regions of rare diseases. This approach, including automated bounding box generation variants, effectively improved classification performance on imbalanced skin image datasets.…”

Section: The Challenge Of Imbalanced Datamentioning

confidence: 99%

See 1 more Smart Citation

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

Alzahrani

2024

Preprint

View full text Add to dashboard Cite

This study introduces SkinLiTE, a lightweight supervised contrastive learning model, tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervised and contrastive learning approaches, which leverages labeled data to learn generalizable representations. This approach is particularly adept at handling the complexities and imbalances inherent in skin lesions datasets. The methodology encompasses a two-phase learning process. In the first phase, SkinLiTE utilizes an encoder network and a projection head to transform and project dermoscopic images into a feature space where contrastive loss is applied, focusing on minimizing intra-class variations while maximizing inter-class differences. The second phase freezes the encoder's weights, leveraging the learned representations for classification through a series of dense and dropout layers. The model was evaluated using three datasets from Skin Cancer ISIC 2019-2020, covering a wide range of skin conditions. SkinLiTE demonstrated superior performance across various metrics—accuracy, AUC , and F1 score—particularly when compared with traditional supervised learning models. Notably, SkinLiTE achieved an accuracy of 0.9087 using AugMix augmentation for binary classification of skin lesions. It also showed comparable results with the state-of-the-art approaches of ISIC challenge without relying on external data, underscoring its efficacy and efficiency. The results highlight the potential of SkinLiTE as a significant step forward in the field of dermatological AI, offering a robust, efficient, and accurate tool for skin lesion detection and classification. Its lightweight architecture and ability to handle imbalanced datasets make it particularly suited for integration into Internet of Medical Things environments, paving the way for enhanced remote patient monitoring and diagnostic capabilities. This research contributes to the evolving landscape of AI in healthcare, demonstrating the impact of innovative learning methodologies in medical image analysis.

show abstract

Section: The Challenge Of Imbalanced Datamentioning

confidence: 99%

Section: The Challenge Of Imbalanced Datamentioning

confidence: 99%

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

Alzahrani

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Gradually, attention mechanisms have become more sophisticated, with the development of models like Transformers, which use self-attention to weigh the importance of different parts of the input data [12][13][14][15][16]. While attention mechanisms have brought significant advancements in the field of CT image classification, they come with certain drawbacks, particularly related to computational complexity and interpretability [17,18]. Therefore, the incorporation of modern MLPs represents an effort to overcome these limitations while harnessing the benefits of deep learning for medical imaging analysis.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the Efficacy of No-Attention Architectures in Computed Tomography Image Classification: A Paradigm Shift

Alzahrani

2024

Mathematics

View full text Add to dashboard Cite

The burgeoning domain of medical imaging has witnessed a paradigm shift with the integration of AI, particularly deep learning, enhancing diagnostic precision and expediting the analysis of Computed Tomography (CT) images. This study introduces an innovative Multilayer Perceptron-driven model, DiagnosticMLP, which sidesteps the computational intensity of attention-based mechanisms, favoring a no-attention architecture that leverages Fourier Transforms for global information capture and spatial gating units for local feature emphasis. This study’s methodology encompasses a sophisticated augmentation and patching strategy at the input level, followed by a series of MLP blocks designed to extract hierarchical features and spatial relationships, culminating in a global average pooling layer before classification. Evaluated against state-of-the-art MLP-based models including MLP-Mixer, FNet, gMLP, and ResMLP across diverse and extensive CT datasets, including abdominal, and chest scans, DiagnosticMLP demonstrated a remarkable ability to converge efficiently, with competitive accuracy, F1 scores, and AUC metrics. Notably, in datasets featuring kidney and abdomen disorders, the model showcased superior generalization capabilities, underpinned by its unique design that addresses the complexity inherent in CT imaging. The findings in terms of accuracy and precision-recall balance posit DiagnosticMLP as an exceptional outperforming alternative to attention-reliant models, paving the way for streamlined, efficient, and scalable AI tools in medical diagnostics, reinforcing the potential for AI-augmented precision medicine without the dependency on attention-based architectures.

show abstract

Autoencoder- Based Multistage Strategy for Class Imbalance in Medical Imaging Analysis - Chest X-rays

Koyyada,

Singh,

Sharma

2024

SN COMPUT. SCI.

View full text Add to dashboard Cite

Class attention to regions of lesion for imbalanced medical image recognition

Cited by 7 publications

References 34 publications

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

Deciphering the Efficacy of No-Attention Architectures in Computed Tomography Image Classification: A Paradigm Shift

Autoencoder- Based Multistage Strategy for Class Imbalance in Medical Imaging Analysis - Chest X-rays

Contact Info

Product

Resources

About