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The coronavirus disease 2019 (COVID-19) pandemic has brought computer-aided diagnosis into the spotlight. COVID-19 computed tomography (CT) images often have redundant background, and the proportion of infected area and healthy areas is ...

Fig. Architecture of the proposed DIEP-Unet.

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• A novel model of dual-path information augmented pyramid Unet network (DIEP-Unet) was proposed for the COVID-19 infection segmentation in this paper.
• A hybrid attention global context-aware (HAGCA) module was proposed, which enhanced ...

Computer vision has emerged as a promising technology with numerous applications in healthcare. This systematic review provides an overview of advancements and challenges associated with computer vision in healthcare. The review highlights the ...

• Comprehensive overview of recent advancements in computer vision for healthcare applications.
• In-depth analysis of algorithms for healthcare computer vision tasks, aiding selection decisions.
• Reviews top computer vision papers in ...

Using Deep Learning in computer-aided diagnosis systems has been of great interest due to its impressive performance in the general domain and medical domain. However, a notable challenge is the lack of explainability of many advanced models, ...

• We propose ItpCtrl-AI, a controllable interpretable method for chest X-ray diagnosis.
• ItpCtrl-AI generates gaze heatmaps to aid in diagnosis and allows user control.
• We introduce Diagnosed-Gaze++ dataset, aligning medical findings ...

Early and accurate diagnosis of lung cancer, a life-threatening disease, is critical to the successful treatment of patients with the disease. On the other hand, it is well known that the integration of computer-aided diagnosis (CAD) systems into ...

• Evaluate the performance of many self-attention-based Vision Transformer models.
• Propose an enhanced FocalNeXt architecture, which integrates ConvNeXt into FocalNet.
• Includes an extensive ablation study, showcasing the strength of ...

Computer-aided diagnosis (CAD) of pituitary microadenoma (PM) can assist doctors in decision-making, leading to improved lesion detection rates and diagnostic accuracy. However, the performance of existing CAD methods for PM detection has been ...

• Multiscale feature selection module to address the loss of feature information.
• The Dual-path Semantic Segmentation Module solves the problem of precision loss.
• Reuse the underlying information to overcome difficult detection of ...

Liver cancer remains a leading cause of global mortality, driving interest in computer-aided diagnosis for liver tumor detection. Existing methods typically focus on individual lesions and avoid the impact of neighboring tumors on diagnostic ...

Fundus images can be obtained non-invasively and be adopted to monitor the follow-up on various fundus diseases, such as high myopia. Therefore, the use of fundus images for the early screening of eye diseases has principal clinical significance. ...

• An attention-based deep network is proposed for pediatric high myopia diagnosis.
• We combine the strengths of both knowledge-guided and data-driven methods.
• Extensive experiments are conducted to prove the effectiveness of our ...

Computer-aided diagnosis (CAD) systems based on deep learning have shown significant potential in lung nodule diagnosis, providing substantial assistance to medical professionals. However, the inherent lack of interpretability in deep learning ...

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• The rough set theory is utilized to represent uncertain annotations of lung nodules.
• We propose a novel rough neuron, the dual-output rough convolutional layer (DOR Conv).
• A region-constraint strategy is designed to embed domain ...

Medical image segmentation is crucial in accurately identifying and delineating regions of interest in medical images, which can inform the diagnosis and treatment of various diseases. Therefore, developing high-performance computer-aided ...

When developing Computer Aided Detection (CAD) systems for Digital Breast Tomosynthesis (DBT), the complexity arising from the volumetric nature of the modality poses significant technical challenges for obtaining large-scale accurate annotations. ...

Fairness in medical AI is increasingly recognized as a crucial aspect of healthcare delivery. While most of the prior work done on fairness emphasizes the importance of equal performance, we argue that decreases in fairness can be either harmful ...

Automated detection of cervical abnormal cells from Thin-prep cytologic test (TCT) images is crucial for efficient cervical abnormal screening using computer-aided diagnosis systems. However, the construction of the detection model is hindered by ...

Medical image segmentation plays an important role in computer-aid diagnosis. In the past years, convolutional neural networks, especially the UNet-based architectures with symmetric U-shape encoder-decoder structure and skip connection, have been widely ...

Inherited retinal diseases (IRDs) are genetic disorders that cause progressive deterioration of the photoreceptors associated with vision loss or blindness. Retinitis pigmentosa (RP) is a rare hereditary ophthalmic disease that initially causes ...

• Robust computer-aided diagnosis and analysis of retinitis pigmentosa disease.
• Utilizing triplet spatial information fusion benefits for pigment sign detection.
• Two individual powerful networks based on multiscale spatial fusion.

Diabetic retinopathy (DR) is a disorder commonly associated with individuals who have diabetes and is caused by excessive glucose levels in the blood, which induce retinal damage. Manual diagnosis of DR by medical experts is time-consuming; hence,...

Accurate segmentation of pulmonary nodule is essential for subsequent pathological analysis and diagnosis. However, current U-Net architectures often rely on a simple skip connection scheme, leading to the fusion of feature maps with different ...

• The Semantic Aggregation Pyramid (SAP) module, based on channel-wise cross-attention mechanism and feature pyramid, is proposed to replaces the skip connections in U-Net. This module mitigates the detrimental effects of simple skip ...

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it combines information from various imaging modalities to provide a more comprehensive understanding of the underlying pathology. Recently, deep learning-...

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• Deep learning-based multimodal fusion techniques for medical classification are reviewed.
• An up-to-date taxonomy of multimodal information fusion techniques is proposed.
• Public datasets of multimodal image classification datasets ...

Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists during diagnosis. Cardiac biopsies require in-depth analysis of the relationships between ...

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• Deep learning system that helps diagnose rejection risk in heart transplant patients.
• System segments endocardium, blood vessels, and inflammation in histological samples.
• Custom attention gate in architecture mirrors pathologist ...

This study proposed an integrated dataset-preparation system for ML-based medical image diagnosis, offering high clinical applicability in various modalities and diagnostic purposes. With the proliferation of ML-based computer-aided diagnosis ...

Deep neural networks (DNNs) have facilitated the development of computer-aided diagnosis (CAD) systems for fundus diseases, helping ophthalmologists to reduce missed diagnoses and misdiagnosis rates. However, the majority of CAD systems are data-...

• We propose an eye-tracking-based HITL CAD system for fundus disease detection.
• We propose a novel DCAMIL model with the contrast learning regularization.
• We introduce the SA and DAN modules into the DCAMIL model.
• We construct ...