Medical Image Analysis: Progress over Two Decades and the Challenges Ahead
By James S. Duncan, Nicholas Ayache. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000
1,309 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/8a7ba32e35eab6a7d86538e0e333df64fb0dca73
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In Artificial Intelligence
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Citations in the corpus
- A review of vessel extraction techniques and algorithms (2004, methodology): “…referred to several surveys on medical image segmentation and analysis in general [McInerney and Terzopoulos 1996; Chen et al. 1999; Ayache 1994; Duncan and Ayache 2000; Clarke et al. 1995], and vessel visualization and quanti.cation in particular [Felkel et al. 2001; Buhler et al. 2002]…”
- The dual-bootstrap iterative closest point algorithm with application to retinal image registration (2003, methodology): “Dual-bootstrap ICP uses the ICP algorithm, which was invented almost simultaneously in the early 1990s by several different research groups [7], [16], [17], [51], [87] and has been used in many applications since then [25], [42], [51], [56].”
- 3D Deep Learning on Medical Images: A Review (2020, background): “In this regard, the process of fine-tuning of 3D CNN models, which are already trained on natural image datasets, can be applied to medical images [14,36,117–119,121,122].”
- Dense image registration through MRFs and efficient linear programming (2008, background): “Medical image analysis [7] is an established domain in computational, mathematical and biological sciences.”
- Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network (2017, background): “ Recent advancement of deep learning has enabled computer vision models to help the doctors to diagnose various problems and in some cases the models have exhibited competitive performance to doctors [3, 4, 5, 6, 7]. Automatic lung cancer diagnosing has several difficulties compared with general comp”
- Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data (2013, background): “In addition, there can be large variability among patients with the same health condition [27].”