AIMC Topic: Diagnosis, Computer-Assisted

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Detecting and classifying lesions in mammograms with Deep Learning.

Scientific reports
In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimat...

Using Machine Learning to Improve the Prediction of Functional Outcome in Ischemic Stroke Patients.

IEEE/ACM transactions on computational biology and bioinformatics
Ischemic stroke is a leading cause of disability and death worldwide among adults. The individual prognosis after stroke is extremely dependent on treatment decisions physicians take during the acute phase. In the last five years, several scores such...

Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome.

IEEE journal of biomedical and health informatics
When training a machine learning algorithm for a supervised-learning task in some clinical applications, uncertainty in the correct labels of some patients may adversely affect the performance of the algorithm. For example, even clinical experts may ...

Using echo state networks for classification: A case study in Parkinson's disease diagnosis.

Artificial intelligence in medicine
Despite having notable advantages over established machine learning methods for time series analysis, reservoir computing methods, such as echo state networks (ESNs), have yet to be widely used for practical data mining applications. In this paper, w...

Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study.

Scientific reports
We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digita...

Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images.

Computers in biology and medicine
Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the location of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intra-observer variabilities. Thus, a...

Learning-based classification of informative laryngoscopic frames.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Early-stage diagnosis of laryngeal cancer is of primary importance to reduce patient morbidity. Narrow-band imaging (NBI) endoscopy is commonly used for screening purposes, reducing the risks linked to a biopsy but at the co...

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (...

Deep Learning in Radiology: Does One SizeĀ Fit All?

Journal of the American College of Radiology : JACR
Deep learning (DL) is a popular method that is used to perform many important tasks in radiology and medical imaging. Some forms of DL are able to accurately segment organs (essentially, trace the boundaries, enabling volume measurements or calculati...