Computer-aided diagnosis: how to move from the laboratory to the clinic.

By B. van Ginneken, C. Schaefer-Prokop, Mathias Prokop. Radiology, 2011

303 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0a6c10dcb6af333b7ce82d254645fcb9faaeb2c1

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration (2016, methodology): “A 57 number of CAD systems have been approved by the Food and Drug Administration (FDA) for 58 lesion detection and volumetric analysis in mammography, chest radiography, and chest 59 computed tomography.(11) 60 61”
AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem? (2020, background): “apy and bio-marker measurement systems [1], [2], [3],”
Artificial intelligence in radiology (2018, unknown): “As a result, only a few have been translated into the clinic 39 .”
Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks (2018, unknown): “Organ segmentation is a crucially important step for computer-assisted diagnostic and biomarker measurement systems [1].”
Computer-aided classification of lung nodules on computed tomography images via deep learning technique (2015, unknown): “One of the major purposes of CAD is to automatically differentiate the malignant/benign nature of tumors/lesions based on numeric image features to assist decision-making whenever there is diagnostic uncertainty and disagreement.(5,10) A traditional CAD scheme”
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, background): “M EDICAL image segmentation aims to delineate the interested anatomical structures like organs and tumors from the original images by labeling each pixel into a certain class, which is a basic and important step for many clinical approaches like computer-aided diagnosis, treatment planning and radia”
A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations (2017, background): “[15] suggest that computer assistance for interpretation and diagnosis can be more useful by supplying a computer-generated ranking of lesion annotations rather than by diagnosing the lesions itself [9].”
A deep learning framework for supporting the classification of breast lesions in ultrasound images (2017, unknown): “of medical image interpretation and diagnosis[4, 5, 6, 7, 8, 9, 10, 11, 12, 13].”
Deep learning in generating radiology reports: A survey (2020, unknown): “Traditionally, computer-aided detection (CAD) systems interpret medical images automatically to offer an objective diagnosis and assist radiologists [2].”