AIMC Topic: Image Processing, Computer-Assisted

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Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review.

PloS one
Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborn...

Using machine learning to distinguish between authentic and imitation Jackson Pollock poured paintings: A tile-driven approach to computer vision.

PloS one
Jackson Pollock's abstract poured paintings are celebrated for their striking aesthetic qualities. They are also among the most financially valued and imitated artworks, making them vulnerable to high-profile controversies involving Pollock-like pain...

Omnidirectional image super-resolution via position attention network.

Neural networks : the official journal of the International Neural Network Society
For convenient transmission, omnidirectional images (ODIs) usually follow the equirectangular projection (ERP) format and are low-resolution. To provide better immersive experience, omnidirectional image super resolution (ODISR) is essential. However...

Deep recognition of rice disease images: how many training samples do we really need?

Journal of the science of food and agriculture
BACKGROUND: With the rapid development of deep learning, the recognition of rice disease images using deep neural networks has become a hot research topic. However, most previous studies only focus on the modification of deep learning models, while l...

Spectrum-based deep learning framework for dermatological pigment analysis and simulation.

Computers in biology and medicine
BACKGROUND: Deep learning in dermatology presents promising tools for automated diagnosis but faces challenges, including labor-intensive ground truth preparation and a primary focus on visually identifiable features. Spectrum-based approaches offer ...

Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX.

Nature communications
The growing scale and dimensionality of multiplexed imaging require reproducible and comprehensive yet user-friendly computational pipelines. TRACERx-PHLEX performs deep learning-based cell segmentation (deep-imcyto), automated cell-type annotation (...

RDLR: A Robust Deep Learning-Based Image Registration Method for Pediatric Retinal Images.

Journal of imaging informatics in medicine
Retinal diseases stand as a primary cause of childhood blindness. Analyzing the progression of these diseases requires close attention to lesion morphology and spatial information. Standard image registration methods fail to accurately reconstruct pe...

Multi-label classification of retinal diseases based on fundus images using Resnet and Transformer.

Medical & biological engineering & computing
Retinal disorders are a major cause of irreversible vision loss, which can be mitigated through accurate and early diagnosis. Conventionally, fundus images are used as the gold diagnosis standard in detecting retinal diseases. In recent years, more a...

FDAA: A feature distribution-aware transferable adversarial attack method.

Neural networks : the official journal of the International Neural Network Society
In recent years, the research on transferable feature-level adversarial attack has become a hot spot due to attacking unknown deep neural networks successfully. But the following problems limit its transferability. Existing feature disruption methods...

Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images.

Journal of translational medicine
BACKGROUND: Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions.