The classification of bioimages plays an important role in several biological studies, such as subcellular localisation, phenotype identification and other types of histopathological examinations. The objective of the present study was ...

• Proposed method is useful for the classification of bioimages across diverse datasets.

To fully enhance the feature extraction capabilities of deep learning models, so as to accurately diagnose coronavirus disease 2019 (COVID-19) based on chest CT images, a densely connected attention network (...

• A densely connected attention feature extraction block was constructed.
• An ...

Acute ischemic stroke is one of the leading causes of death and long-term disability worldwide. It occurs when a blood clot blocks an artery that supplies blood to the brain tissue. Segmentation of acute ischemic stroke lesions plays a ...

• Computed tomography perfusion (CTP) is an efficient imaging modality for ischemic stroke lesion segmentation.

Coronavirus disease 2019 (COVID-19) has caused more than 3 million deaths and infected more than 170 million individuals all over the world. Rapid identification of patients with COVID-19 is the key to control transmission and prevent ...

• An available dataset COVID-CTx is established.
• •A light-weighted hybrid neural network: Depthwise ...

The automatic segmentation of medical images has made continuous progress due to the development of convolutional neural networks (CNNs) and attention mechanism. However, previous works usually explore the attention features of a ...

• The CSE block was proposed to learn the long-range dependencies, and to perform weighting on channels.

Novel coronavirus disease 2019 (COVID-19) is an infectious disease that spreads very rapidly and threatens the health of billions of people worldwide. With the number of cases increasing rapidly, most countries are facing the problem ...

• We built a new deep learning model (ULNet) and applied it to two classification and three classification tasks.

Colonoscopy remains the gold-standard screening for colorectal cancer. However, significant miss rates for polyps have been reported, particularly when there are multiple small adenomas. This presents an ...

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• Automatic polyp segmentation can support clinicians to reduce polyp miss rates.

In recent years, vast developments in Computer-Aided Diagnosis (CAD) for skin diseases have generated much interest from clinicians and other eventual end-users of this technology. Introducing clinical domain knowledge to these machine ...

• A novel method for melanoma detection is proposed based on clinically constrained classifier chain and deep learning.

Most existing Electrocardiogram (ECG) classification methods assume that all arrhythmia classes are known during the training phase. In this paper, the problem of learning several successive tasks is addressed, where, in each new task, ...

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• Learning successive ECG Heartbeat classification tasks with new classes.
• We ...

Artificial intelligence (AI) has served humanity in many applications since its inception. Currently, it dominates the imaging field—in particular, image classification. The task of image classification became ...

• Use of PRISMA model for search strategy.
• Three types of hybrid deep learning ...

Diabetic retinopathy (DR) has become one of the major causes of blindness. Due to the increased prevalence of diabetes worldwide, diabetic patients exhibit high probabilities of developing DR. There is a need to develop a labor-less ...

Infertility is a common disorder affecting 20% of couples worldwide. Furthermore, 40% of all cases are related to male infertility. The first step in the determination of male infertility is semen analysis. The morphology, ...

• Six CNN models were created for automating the morphological classification of sperm images.

Histopathological images provide a gold standard for cancer recognition and diagnosis. Existing approaches for histopathological image classification are supervised learning methods that demand a large amount of labeled data to obtain ...

• We propose a novel semi-supervised method Semi-HIC for histopathological image classification with limited labeled data.

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is characterized by the heart's beating in an uncoordinated manner. In clinical studies, patients often do not have visible symptoms during AF, and hence it is ...

• A novel time-frequency domain deep learning-based approach is proposed to detect AF and classify terminating and non-terminating AF episodes using ECG ...

Correct virtual reconstruction of a defective skull is a prerequisite for successful cranioplasty and its automatization has the potential for accelerating and standardizing the clinical workflow. This work provides a deep learning-...

• Defective skull patch reconstruction and cranial implant design are solved jointly using multi-task learning.

In patients with kidney failure with replacement therapy (KFRT), optimizing anemia management in these patients is a challenging problem because of the complexities of the underlying diseases and heterogeneous responses to ...

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• Anemia management is challenging in patients with kidney failure with replacement therapy (KFRT).