Machine learning approaches in medical image analysis: From detection to diagnosis

By Marleen de Bruijne. Medical Image Anal., 2016

319 citations recorded by the source corpus.

Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c1124755f5ea760cbe6b5aa61189cde3da06d1a5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Machine learning and deep learning approach for medical image analysis: diagnosis to detection (2022, methodology): “Brain tumor was detected using six classifiers and Transfer Learning (TL) techniques for image segmentation with Magnetic Resonance Imaging (MRI) of the brain [28].”
Recent advances and clinical applications of deep learning in medical image analysis (2021, background): “Transfer learning has become a cornerstone for image classification tasks (de Bruijne, 2016), which has proven effective for a variety of modalities, including CT (Kumar et al., 2017; Shin et al., 2016), mammography (Huynh et al., 2016), MRI (Kumar et al., 2017), ultrasonography (Liu et al., 2017;…”