AIMC Topic: Image Interpretation, Computer-Assisted

Clear Filters Showing 1481 to 1490 of 3033 articles

The detection of lung cancer using massive artificial neural network based on soft tissue technique.

BMC medical informatics and decision making
BACKGROUND: A proposed computer aided detection (CAD) scheme faces major issues during subtle nodule recognition. However, radiologists have not noticed subtle nodules in beginning stage of lung cancer while a proposed CAD scheme recognizes non subtl...

Automated analysis and detection of abnormalities in transaxial anatomical cardiovascular magnetic resonance images: a proof of concept study with potential to optimize image acquisition.

The international journal of cardiovascular imaging
The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a scan are protocolled based on clinical details. Adapting this to unexpected findings identified early ...

Thyroid Nodule Classification for Physician Decision Support Using Machine Learning-Evaluated Geometric and Morphological Features.

Sensors (Basel, Switzerland)
The classification of thyroid nodules using ultrasound (US) imaging is done using the Thyroid Imaging Reporting and Data System (TIRADS) guidelines that classify nodules based on visual and textural characteristics. These are composition, shape, size...

Tumor segmentation in automated whole breast ultrasound using bidirectional LSTM neural network and attention mechanism.

Ultrasonics
Accurate breast mass segmentation of automated breast ultrasound (ABUS) is a great help to breast cancer diagnosis and treatment. However, the lack of clear boundary and significant variation in mass shapes make the automatic segmentation very challe...

Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks.

Gastroenterology
BACKGROUND AND AIMS: Endoscopic disease activity scoring in ulcerative colitis (UC) is useful in clinical practice but done infrequently. It is required in clinical trials, where it is expensive and slow because human central readers are needed. A ma...

Artificial Intelligence Applied to Breast MRI for Improved Diagnosis.

Radiology
Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to improve diagnoses that may improve patient tr...

PyHIST: A Histological Image Segmentation Tool.

PLoS computational biology
The development of increasingly sophisticated methods to acquire high-resolution images has led to the generation of large collections of biomedical imaging data, including images of tissues and organs. Many of the current machine learning methods th...

Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival.

Clinical physiology and functional imaging
INTRODUCTION: Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods ca...

Imaging Database Preparation for Machine Learning.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes

Automated detection of cerebral microbleeds in MR images: A two-stage deep learning approach.

NeuroImage. Clinical
Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. However, automated detectio...