AIMC Topic: Image Interpretation, Computer-Assisted

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Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images.

IEEE transactions on bio-medical engineering
OBJECTIVE: Left ventricular (LV) volume estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address a direct LV volume prediction task.

Classification of the trabecular bone structure of osteoporotic patients using machine vision.

Computers in biology and medicine
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
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
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
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
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
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
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)
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
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 ...