Current status and future potential of computer-aided diagnosis in medical imaging.

By Kunio Doi. British Journal of Radiology, 2005

490 citations recorded by the source corpus.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4e7408656eb1ac2ba5bd2929b2c00ea88b9a5098

How researchers see this paper

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

In Pulmonary and Respiratory Medicine

1. Ronald M. Summers
2. Shijun Wang
3. Kunio Doi
4. B. van Ginneken
5. Kenji Suzuki

In Radiology, Nuclear Medicine and Imaging

1. Ronald M. Summers
2. Shijun Wang
3. Kunio Doi
4. B. van Ginneken

Citations in the corpus

• Medical Image Synthesis with Generative Adversarial Networks for Tissue Recognition (2018, background): “Tissue recognition is one of the most fundamental procedures for many medical imaging applications, especially for computer-aided diagnosis (CAD), which provides a second opinion by computer and assists the interpretation and diagnosis of diseases for radiologists [1].”
• Recent advances and clinical applications of deep learning in medical image analysis (2021, background): “CADx-generated classification score serves as an objective “second opinion” for radiologists to make the final decisions regarding a disease (Doi, 2007; Doi, 2005).”
• Computer-aided diagnosis in medical imaging: Historical review, current status and future potential (2007, background): “Because vertebral fractures can be detected reliably by computer on lateral chest radiographs, radiologists’ accuracy in the detection of vertebral fractures would be improved by the use of CAD, and thus early diagnosis of osteoporosis would become possible.”
• Machine Learning and Radiology (2012, background): “In many radiology applications, e.g. mammography and colon CAD, computerized CADx systems have shown comparable, or even higher, performance compared with well-trained and experienced radiologists and technologists (Cheng et al., 2003; Doi, 2005, 2007; Giger et al., 2001).”
• Automatic Tuberculosis Screening Using Chest Radiographs (2013, background): “The result is that CAD systems can successfully assist radiologists in diagnosing lung cancer [20].”