AIMC Topic: Image Processing, Computer-Assisted

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Robustness evaluation against corruptions for Optical Diffraction Tomography-based classifiers.

Computers in biology and medicine
Optical Diffraction Tomography (ODT) is a promising technique for three-dimensional imaging, but practical use demands rigorous robustness testing due to real-world noise factors. Despite the growing importance of machine learning safety, robustness ...

Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization.

Scientific reports
Breast cancer remains the most prevalent cause of cancer-related mortality among women worldwide, with an estimated incidence exceeding 500,000 new cases annually. Timely diagnosis is vital for enhancing therapeutic outcomes and increasing survival p...

FSF-ViT: Image augmentation and adaptive global-local feature fusion for Few-Shot Food classification.

Food chemistry
We proposed FSF-ViT, a Vision Transformer (ViT)-based model integrating image augmentation and adaptive global-local feature fusion, for Few-Shot Food (FSF) classification. The proposed method focused on training with limited food images to reduce da...

A preprocessing method based on 3D U-Net for abdomen segmentation.

Computers in biology and medicine
Deep learning methods have made significant progress in the field of biomedical automatic segmentation but still open to developments, especially due to the insufficient use of preprocessing methods. In this study, a pre-processing step is proposed b...

Accurate and real-time brain tumour detection and classification using optimized YOLOv5 architecture.

Scientific reports
The brain tumours originate in the brain or its surrounding structures, such as the pituitary and pineal glands, and can be benign or malignant. While benign tumours may grow into neighbouring tissues, metastatic tumours occur when cancer from other ...

Multimodal deep learning for cephalometric landmark detection and treatment prediction.

Scientific reports
In orthodontics and maxillofacial surgery, accurate cephalometric analysis and treatment outcome prediction are critical for clinical decision-making. Traditional approaches rely on manual landmark identification, which is time-consuming and subject ...

Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis.

Nature communications
Activity recognition in live-cell imaging is labor-intensive and requires significant human effort. Existing automated analysis tools are largely limited in versatility. We present the Intelligent Vesicle Exocytosis Analysis (IVEA) platform, an Image...

Fusion of bio-inspired optimization and machine learning for Alzheimer's biomarker analysis.

Computers in biology and medicine
Identification of Alzheimer's Disease (AD), especially in its early phases, presents significant challenges due to the nonexistence of reliable biomarkers and effective treatments. Clinical trials for AD medications also suffer from high failure rate...

Tiny-objective segmentation for spot signs on multi-phase CT angiography via contrastive learning with dynamic-updated positive-negative memory banks.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Presence of spot sign on CT Angiography (CTA) is associated with hematoma growth in patients with intracerebral hemorrhage. Measuring spot sign volume over time may aid to predict hematoma expansion. Due to the difficulties ...

ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases.

Scientific reports
The increasing global population, coupled with the diminishing availability of arable land, has rendered the challenge of ensuring food security more pronounced. The prompt and precise identification of plant diseases is essential for reducing crop l...