AIMC Topic: Prostate

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Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks.

IEEE transactions on medical imaging
Accurate segmentation of pelvic organs (i.e., prostate, bladder, and rectum) from CT image is crucial for effective prostate cancer radiotherapy. However, it is a challenging task due to: 1) low soft tissue contrast in CT images and 2) large shape an...

Automatic polyp frame screening using patch based combined feature and dictionary learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Polyps in the colon can potentially become malignant cancer tissues where early detection and removal lead to high survival rate. Certain types of polyps can be difficult to detect even for highly trained physicians. Inspired by aforementioned proble...

Automated Gleason grading of prostate cancer tissue microarrays via deep learning.

Scientific reports
The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibili...

Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging.

International journal of computer assisted radiology and surgery
PURPOSE: We propose an approach of 3D convolutional neural network to segment the prostate in MR images.

Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values.

Radiology
Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean apparent diffusion coefficient (ADC), and radiologist assessment for characterization of prostate lesions detected during prospective MRI interpretation. Materials an...