AIMC Topic: Humans

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Deep transfer learning based hierarchical CAD system designs for SFM images.

Journal of medical engineering & technology
Present work involves rigorous experimentation for classification of mammographic masses by employing four deep transfer learning models using hierarchical framework. Experimental work is carried on 518 SFM images of DDSM dataset with 208, 150 and 16...

A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features.

Journal of medical engineering & technology
Developing a robust and effective technique is crucial for interpreting a user's brainwave signals accurately in the realm of biomedical signal processing. The variability and uncertainty present in EEG patterns over time, compounded by noise, pose n...

An arrhythmia classification using a deep learning and optimisation-based methodology.

Journal of medical engineering & technology
The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interference. The preprocessed signals ar...

Development and multicenter validation of machine learning models for predicting postoperative pulmonary complications after neurosurgery.

Chinese medical journal
BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery.

Prediction of Treatment Outcome in Bipolar Disorder: When Can We Expect Clinical Relevance?

Biological psychiatry
Long-term pharmacological treatment is the cornerstone of the management of bipolar disorder (BD). Clinicians typically select mood-stabilizing medications from among several options through trial and error. This process could be optimized by using r...

RADEX: a rule-based clinical and radiology data extraction tool demonstrated on thyroid ultrasound reports.

European radiology
OBJECTIVES: Radiology reports contain valuable information for research and audits, but relevant details are often buried within free-text fields. This makes them challenging and time-consuming to extract for secondary analyses, including training ar...

A multicenter diagnostic study of thyroid nodule with Hashimoto's thyroiditis enabled by Hashimoto's thyroiditis nodule-artificial intelligence model.

European radiology
OBJECTIVE: This study aimed to develop a Hashimoto's thyroiditis nodule-artificial intelligence (HTN-AI) model to optimize the diagnosis of thyroid nodules with Hashimoto's thyroiditis (HT) of which the efficiency and accuracy remain challenging.

Figure plagiarism and manipulation, an under-recognised problem in academia.

European radiology
Academic plagiarism undermines the integrity of scientific research. While text-based plagiarism detection tools are widely used, the rise of artificial intelligence (AI) has introduced new challenges, particularly in text and image generation and ma...

Improved segmentation of hepatic vascular networks in ultrasound volumes using 3D U-Net with intensity transformation-based data augmentation.

Medical & biological engineering & computing
Accurate three-dimensional (3D) segmentation of hepatic vascular networks is crucial for supporting ultrasound-mediated theranostics for liver diseases. Despite advancements in deep learning techniques, accurate segmentation remains challenging due t...

Unsupervised cross-modality domain adaptation via source-domain labels guided contrastive learning for medical image segmentation.

Medical & biological engineering & computing
Unsupervised domain adaptation (UDA) offers a promising approach to enhance discriminant performance on target domains by utilizing domain adaptation techniques. These techniques enable models to leverage knowledge from the source domain to adjust to...