AIMC Topic: Image Processing, Computer-Assisted

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Lightweight grape leaf disease recognition method based on transformer framework.

Scientific reports
Grape disease image recognition is an important part of agricultural disease detection. Accurately identifying diseases allows for timely prevention and control at an early stage, which plays a crucial role in reducing yield losses. This study addres...

Quantitative image analysis of the extracellular matrix of esophageal squamous cell carcinoma and high grade dysplasia via two-photon microscopy.

Scientific reports
Squamous cell carcinoma (SCC) and high-grade dysplasia (HGD) are two different pathological entities; however, they sometimes share similarities in histological structure depending on the context. Thus, distinguishing between the two may require care...

Multi-module UNet++ for colon cancer histopathological image segmentation.

Scientific reports
In the pathological diagnosis of colorectal cancer, the precise segmentation of glandular and cellular contours serves as the fundamental basis for achieving accurate clinical diagnosis. However, this task presents significant challenges due to compl...

Lightweight wavelet convolutional network for guidewire segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate guidewire segmentation is crucial for the success of vascular interventional procedures. Existing methods rely on a large number of parameters, making it difficult to balance performance and model size. In addition, the difficulty of collect...

DeepHybrid-CNN: A hybrid approach for pre-processing of skin cancer images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In the current technological era, digital imaging is ubiquitous, and it serves a crucial purpose in the realm of medical research. Skin cancer is one of the most common types of cancer, and its early diagnosis is essential to reduce the mortality rat...

Development of a deep learning based approach for multi-material decomposition in spectral CT: a proof of principle in silico study.

Scientific reports
Conventional approaches to material decomposition in spectral CT face challenges related to precise algorithm calibration across imaged conditions and low signal quality caused by variable object size and reduced dose. In this proof-of-principle stud...

Machine learning training data: over 500,000 images of butterflies and moths (Lepidoptera) with species labels.

Scientific data
Deep learning models can accelerate the processing of image-based biodiversity data and provide educational value by giving direct feedback to citizen scientists. However, the training of such models requires large amounts of labelled data and not al...

Semi-supervised medical image segmentation based on multi-stage iterative training and high-confidence pseudo-labeling.

Biomedical physics & engineering express
Due to the scarcity and high cost of pixel-level annotations for training data, semi-supervised learning has gradually become a key solution. Most existing methods rely on consistency regularization and pseudo-label generation, often adopting multi-b...

Advancing fishery dependent and independent habitat assessments using automated image analysis: A fisheries management agency case study.

PloS one
Advances in artificial intelligence and machine learning have revolutionised data analysis, including in the field of marine and fisheries sciences. However, many fisheries agencies manage sensitive or proprietary data that cannot be shared externall...

YOLO-LeafNet: a robust deep learning framework for multispecies plant disease detection with data augmentation.

Scientific reports
Plant diseases significantly harm crops, resulting in significant economic losses across the globe. In order to reduce the harm that these diseases produce, plant diseases must be diagnosed accurately and timely manner. In this work, a YOLO-LeafNet a...