Early detection and localization of prostate tumors pose a challenge to the medical community. Several imaging techniques, including PET, have shown some success. But no robust and accurate solution has yet been reached. This work aims to detect pros...
Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of tumor area, tumor type and grades. This paper presents automatic glioma tumor grade identification f...
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...
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).
IMPORTANCE: Proper evaluation of the performance of artificial intelligence techniques in the analysis of digitized medical images is paramount for the adoption of such techniques by the medical community and regulatory agencies.
Multi-parametric MRI (mp-MRI) is considered the best non-invasive imaging modality for diagnosing prostate cancer (PCa). However, mp-MRI for PCa diagnosis is currently limited by the qualitative or semi-quantitative interpretation criteria, leading t...
High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. Conventional survival prediction based on clinical information is subjective and could be inacc...
OBJECTIVE: To develop different radiomic models based on the magnetic resonance imaging (MRI) radiomic features and machine learning methods to predict early intensity-modulated radiation therapy (IMRT) response, Gleason scores (GS) and prostate canc...
OBJECTIVES: Preoperative, noninvasive prediction of the meningioma grade is important because it influences the treatment strategy. The purpose of this study was to evaluate the role of radiomics features of postcontrast T1-weighted images (T1C), app...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Nov 13, 2018
We propose to discriminate the pathological grades directly on digital mammograms instead of pathological images. An end-to-end learning algorithm based on the combined multi-level features is proposed. Low-level features are extracted and selected b...