AIMC Topic: ROC Curve

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A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images.

IEEE transactions on medical imaging
Obtaining the complete segmentation map of retinal lesions is the first step toward an automated diagnosis tool for retinopathy that is interpretable in its decision-making. However, the limited availability of ground truth lesion detection maps at a...

MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features.

IEEE transactions on medical imaging
In this paper, we propose bag of adversarial features (BAFs) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRIs) (obtained within one month of injury) by incorporating unsupervised feature...

Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma.

Oral oncology
OBJECTIVES: To develop and validate an algorithm to predict occult nodal metastasis in clinically node negative oral cavity squamous cell carcinoma (OCSCC) using machine learning. To compare algorithm performance to a model based on tumor depth of in...

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method.

European journal of radiology
PURPOSE: To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa).

Deep Learning versus Conventional Machine Learning for Detection of Healthcare-Associated Infections in French Clinical Narratives.

Methods of information in medicine
OBJECTIVE: The objective of this article was to compare the performances of health care-associated infection (HAI) detection between deep learning and conventional machine learning (ML) methods in French medical reports.

A dense multi-path decoder for tissue segmentation in histopathology images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Segmenting different tissue components in histopathological images is of great importance for analyzing tissues and tumor environments. In recent years, an encoder-decoder family of convolutional neural networks has increasi...

Neural Networks for Deep Radiotherapy Dose Analysis and Prediction of Liver SBRT Outcomes.

IEEE journal of biomedical and health informatics
Stereotactic body radiation therapy (SBRT) is a relatively novel treatment modality, with little post-treatment prognostic information reported. This study proposes a novel neural network based paradigm for accurate prediction of liver SBRT outcomes....

Random forest classifiers aid in the detection of incidental osteoblastic osseous metastases in DEXA studies.

International journal of computer assisted radiology and surgery
PURPOSE: Dual-energy X-ray absorptiometry (DEXA) studies are used for screening patients for low bone mineral density (BMD). Patients with breast and prostate cancer are often treated with hormone-altering drugs that result in low BMD. These patients...