AIMC Topic: Image Processing, Computer-Assisted

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MFDF-UNet: Multiscale feature depth-enhanced fusion network for colony adhesion image segmentation.

Journal of microbiological methods
Colony counting plays a crucial role in evaluating food quality and safety. The segmentation of colony adhesion images can significantly enhance the accuracy of food safety assessments. To achieve high-precision segmentation of colony adhesion images...

A flow pattern recognition method for gas-liquid two-phase flow based on dilated convolutional channel attention mechanism.

PloS one
Addressing the issue of insufficient key feature extraction leading to low recognition rates in existing deep learning-based flow pattern identification methods, this paper proposes a novel flow pattern image recognition model, Enhanced DenseNet with...

Systematic review of generative adversarial networks (GANs) in cell microscopy: Trends, practices, and impact on image augmentation.

PloS one
Cell microscopy is the main tool that allows researchers to study microorganisms and plays a key role in observing and understanding the morphology, interactions, and development of microorganisms. However, there exist limitations in both the techniq...

MVT-Net: A novel cervical tumour segmentation using multi-view feature transfer learning.

PloS one
Cervical cancer is one of the most aggressive malignant tumours of the reproductive system, posing a significant global threat to women's health. Accurately segmenting cervical tumours in MR images remains a challenging task due to the complex charac...

Uncertainty-based cardiac image registration using variational autoencoder with nonuniformly spaced control points.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The Variational Bayesian (VB) image registration model has garnered recent attention for its ability to offer uncertainty, particularly in the context of cardiac motion estimation. Nonetheless, several challenges have plague...

BrainNet-GAN: Generative Adversarial Graph Convolutional Network for Functional Brain Network Synthesis from Routine Clinical Brain Structural T1-Weighted Sequence.

Brain topography
Functional brain network (FBN) derived from functional Magnetic Resonance Imaging (fMRI) has promising prospects in clinical research, but fMRI is not a routine acquisition data, which limits its popularity in clinical applications. Therefore, it is ...

Fine-grained image generation with EEG multi-level semantics.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Decoding visual information from electroencephalography (EEG) signals is crucial in neuroscience and artificial intelligence. While existing methods have been able to extract high-level features such as object categories, th...

Medical application of deep-learning-based head pose estimation from RGB image sequence.

Computers in biology and medicine
Recently, telemedicine has allowed doctor-to-patient or doctor-to-doctor consultations to tackle traditional problems: the COVID-19 pandemic, remote areas, long-time usage per visit, and dependence on family members in transportation. Nevertheless, f...

SE-ATT-YOLO- A deep learning driven ultrasound based respiratory motion compensation system for precision radiotherapy.

Computers in biology and medicine
OBJECTIVE: The therapeutic management of neoplasm employs high level energy beam to ablate malignant cells, which can cause collateral damage to adjacent normal tissue. Furthermore, respiration-induced organ motion, during radiotherapy can lead to si...

CNN-extracted features generate synthetic fMRI responses to unseen images.

Vision research
Inspired by biological vision, convolutional neural networks (CNNs) have tackled challenging image recognition problems once considered the sole purview of human expertise. In turn, CNNs are now widely used as a framework for studying human vision. T...