AIMC Topic: Image Processing, Computer-Assisted

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Detecting Anomaly Event in Video Based on Generative Adversarial Network.

Computational intelligence and neuroscience
Anomaly detection in videos is a challenging computer vision problem. Existing state-of-the-art video anomaly detection methods mainly focus on the structural design of deep neural networks to obtain performance improvements. Different from the main ...

Objective assessment of segmentation models for thyroid ultrasound images.

Journal of ultrasound
Ultrasound features related to thyroid lesions structure, shape, volume, and margins are considered to determine cancer risk. Automatic segmentation of the thyroid lesion would allow the sonographic features to be estimated. On the basis of clinical ...

A general deep learning framework for neuron instance segmentation based on Efficient UNet and morphological post-processing.

Computers in biology and medicine
Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental step for many biological studies. However, the excellent performance of the neural networks requires ...

Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation.

IEEE transactions on pattern analysis and machine intelligence
Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmentation, i.e., training accurate semantic segmentation models using bound...

Symmetry-Aware Deep Learning for Cerebral Ventricle Segmentation With Intra-Ventricular Hemorrhage.

IEEE journal of biomedical and health informatics
Cerebral ventricles are one of the prominent structures in the brain, segmenting which can provide rich information for brain-related disease diagnosis. Unfortunately, cerebral ventricle segmentation in complex clinical cases, such as in the coexiste...

U-DAVIS-Deep Learning Based Arm Venous Image Segmentation Technique for Venipuncture.

Computational intelligence and neuroscience
Arm Venous Segmentation plays a crucial role in smart venipuncture. The difficulties faced in locating veins for intravenous procedures can be diminished using computer vision for vein imaging. To facilitate this, a high-resolution dataset consisting...

Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning.

Computational and mathematical methods in medicine
RESULTS: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accur...

MLRD-Net: 3D multiscale local cross-channel residual denoising network for MRI-based brain tumor segmentation.

Medical & biological engineering & computing
The precise segmentation of multimodal MRI images is the primary stage of tumor diagnosis and treatment. Current segmentation strategies often underutilize multiscale features, which can easily lead to loss of contextual information, reduction of low...

CardioVinci: building blocks for virtual cardiac cells using deep learning.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Advances in electron microscopy (EM) such as electron tomography and focused ion-beam scanning electron microscopy provide unprecedented, three-dimensional views of cardiac ultrastructures within sample volumes ranging from hundreds of nanometres to ...