AIMC Topic:
Image Interpretation, Computer-Assisted
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Classification of the trabecular bone structure of osteoporotic patients using machine vision.
Computers in biology and medicine
Oct 13, 2017
Osteoporosis is a common bone disease which often leads to fractures. Clinically, the major challenge for the automatic diagnosis of osteoporosis is the complex architecture of bones. The clinical diagnosis of osteoporosis is conventionally done usin...
Quality of clinical brain tumor MR spectra judged by humans and machine learning tools.
Magnetic resonance in medicine
Oct 10, 2017
PURPOSE: To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors.
A dictionary learning approach for human sperm heads classification.
Computers in biology and medicine
Oct 10, 2017
BACKGROUND AND OBJECTIVE: To diagnose infertility in men, semen analysis is conducted in which sperm morphology is one of the factors that are evaluated. Since manual assessment of sperm morphology is time-consuming and subjective, automatic classifi...
Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures.
Computers in biology and medicine
Oct 6, 2017
Alzheimer's disease (AD) has been not only a substantial financial burden to the health care system but also an emotional burden to patients and their families. Making accurate diagnosis of AD based on brain magnetic resonance imaging (MRI) is becomi...
A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.
PloS one
Oct 6, 2017
Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segme...
Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches.
AJNR. American journal of neuroradiology
Oct 5, 2017
Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challeng...
Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI.
Medical & biological engineering & computing
Sep 26, 2017
Precise segmentation of stroke lesions from brain magnetic resonance (MR) images poses a challenging task in automated diagnosis. In this paper, we proposed a new method called watershed-based lesion segmentation algorithm (WLSA), which is a novel in...
Artificial intelligence for breast cancer screening: Opportunity or hype?
Breast (Edinburgh, Scotland)
Sep 20, 2017
Interpretation of mammography for breast cancer (BC) screening can confer a mortality benefit through early BC detection, can miss a cancer that is present or fast growing, or can result in false-positives. Efforts to improve screening outcomes have ...
Individualized prediction of psychosis in subjects with an at-risk mental state.
Schizophrenia research
Sep 19, 2017
Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and ...
A deep learning framework for supporting the classification of breast lesions in ultrasound images.
Physics in medicine and biology
Sep 15, 2017
In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a ...
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