AIMC Topic: Image Processing, Computer-Assisted

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Fast SPECT/CT planar bone imaging enabled by deep learning enhancement.

Medical physics
BACKGROUND: The application of deep learning methods in rapid bone scintigraphy is increasingly promising for minimizing the duration of SPECT examinations. Recent works showed several deep learning models based on simulated data for the synthesis of...

Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G.

Medical & biological engineering & computing
Diabetic retinopathy disease contains lesions (e.g., exudates, hemorrhages, and microaneurysms) that are minute to the naked eye. Determining the lesions at pixel level poses a challenge as each pixel does not reflect any semantic entities. Furthermo...

BiliQML: a supervised machine-learning model to quantify biliary forms from digitized whole slide liver histopathological images.

American journal of physiology. Gastrointestinal and liver physiology
The progress of research focused on cholangiocytes and the biliary tree during development and following injury is hindered by limited available quantitative methodologies. Current techniques include two-dimensional standard histological cell-countin...

All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols a...

Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas.

Sensors (Basel, Switzerland)
Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite significant advancements, there are some limitations that cannot be ignored. Presenting all objects capt...

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection.

Scientific reports
Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease's therapy is significantly impacted by early identification of this ...

An innovative breast cancer detection framework using multiscale dilated densenet with attention mechanism.

Network (Bristol, England)
Cancer-related deadly diseases affect both developed and underdeveloped nations worldwide. Effective network learning is crucial to more reliably identify and categorize breast carcinoma in vast and unbalanced image datasets. The absence of early can...

Using machine learning for chemical-free histological tissue staining.

Journal of histotechnology
Hematoxylin and eosin staining can be hazardous, expensive, and prone to error and variability. To circumvent these issues, artificial intelligence/machine learning models such as generative adversarial networks (GANs), are being used to 'virtually' ...

An Intuitionistic Fuzzy C-Means and Local Information-Based DCT Filtering for Fast Brain MRI Segmentation.

Journal of imaging informatics in medicine
Structural and photometric anomalies in the brain magnetic resonance images (MRIs) affect the segmentation performance. Moreover, a sudden change in intensity between two boundaries of the brain tissues makes it prone to data uncertainty, resulting i...

Effect of MR head coil geometry on deep-learning-based MR image reconstruction.

Magnetic resonance in medicine
PURPOSE: To investigate whether parallel imaging-imposed geometric coil constraints can be relaxed when using a deep learning (DL)-based image reconstruction method as opposed to a traditional non-DL method.