Deep Learning in Medical Image Analysis

Overview

Journal Adv Exp Med Biol

Specialties Biology
General Medicine

Date 2020 Feb 8

PMID 32030660

Citations 185

Authors

Heang-Ping Chan

Ravi K Samala

Lubomir M Hadjiiski

Chuan Zhou

Affiliations

Soon will be listed here.

Abstract

Deep learning is the state-of-the-art machine learning approach. The success of deep learning in many pattern recognition applications has brought excitement and high expectations that deep learning, or artificial intelligence (AI), can bring revolutionary changes in health care. Early studies of deep learning applied to lesion detection or classification have reported superior performance compared to those by conventional techniques or even better than radiologists in some tasks. The potential of applying deep-learning-based medical image analysis to computer-aided diagnosis (CAD), thus providing decision support to clinicians and improving the accuracy and efficiency of various diagnostic and treatment processes, has spurred new research and development efforts in CAD. Despite the optimism in this new era of machine learning, the development and implementation of CAD or AI tools in clinical practice face many challenges. In this chapter, we will discuss some of these issues and efforts needed to develop robust deep-learning-based CAD tools and integrate these tools into the clinical workflow, thereby advancing towards the goal of providing reliable intelligent aids for patient care.

Citing Articles

Cost-effectiveness of novel diagnostic tools for idiopathic pulmonary fibrosis in the United States.

Cadham C, Reicher J, Muelly M, Hutton D BMC Health Serv Res. 2025; 25(1):385.

PMID: 40089758 DOI: 10.1186/s12913-025-12506-1.

On the practical, ethical, and legal necessity of clinical Artificial Intelligence explainability: an examination of key arguments.

Blackman J, Veerapen R BMC Med Inform Decis Mak. 2025; 25(1):111.

PMID: 40045339 PMC: 11881432. DOI: 10.1186/s12911-025-02891-2.

Applications of Artificial Intelligence for the Prediction and Diagnosis of Cancer Therapy-Related Cardiac Dysfunction in Oncology Patients.

Scalia I, Pathangey G, Abdelnabi M, Ibrahim O, Abdelfattah F, Pereyra Pietri M Cancers (Basel). 2025; 17(4).

PMID: 40002200 PMC: 11852369. DOI: 10.3390/cancers17040605.

Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.

Ali M, Benfante V, Basirinia G, Alongi P, Sperandeo A, Quattrocchi A J Imaging. 2025; 11(2).

PMID: 39997561 PMC: 11856378. DOI: 10.3390/jimaging11020059.

Predictive modeling with linear machine learning can estimate glioblastoma survival in months based solely on MGMT-methylation status, age and sex.

Maragno E, Ricchizzi S, Winter N, Hellwig S, Stummer W, Hahn T Acta Neurochir (Wien). 2025; 167(1):52.

PMID: 39992425 PMC: 11850473. DOI: 10.1007/s00701-025-06441-7.

References

Petrick N, Sahiner B, Armato 3rd S, Bert A, Correale L, Delsanto S . Evaluation of computer-aided detection and diagnosis systems. Med Phys. 2013; 40(8):087001. PMC: 4108682. DOI: 10.1118/1.4816310. View

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

Samala R, Chan H, Hadjiiski L, Helvie M, Cha K, Richter C . Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms. Phys Med Biol. 2017; 62(23):8894-8908. PMC: 5859950. DOI: 10.1088/1361-6560/aa93d4. View

Cole E, Zhang Z, Marques H, Hendrick R, Yaffe M, Pisano E . Impact of computer-aided detection systems on radiologist accuracy with digital mammography. AJR Am J Roentgenol. 2014; 203(4):909-16. PMC: 4286296. DOI: 10.2214/AJR.12.10187. View

Zech J, Badgeley M, Liu M, Costa A, Titano J, Oermann E . Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med. 2018; 15(11):e1002683. PMC: 6219764. DOI: 10.1371/journal.pmed.1002683. View

Mazurowski M, Buda M, Saha A, Bashir M . Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. J Magn Reson Imaging. 2018; 49(4):939-954. PMC: 6483404. DOI: 10.1002/jmri.26534. View

Litjens G, Kooi T, Bejnordi B, Setio A, Ciompi F, Ghafoorian M . A survey on deep learning in medical image analysis. Med Image Anal. 2017; 42:60-88. DOI: 10.1016/j.media.2017.07.005. View

De Fauw J, Ledsam J, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S . Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018; 24(9):1342-1350. DOI: 10.1038/s41591-018-0107-6. View

Yan K, Wang X, Lu L, Summers R . DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J Med Imaging (Bellingham). 2018; 5(3):036501. PMC: 6052252. DOI: 10.1117/1.JMI.5.3.036501. View

10.

Chan H, Sahiner B, Wagner R, Petrick N . Classifier design for computer-aided diagnosis: effects of finite sample size on the mean performance of classical and neural network classifiers. Med Phys. 2000; 26(12):2654-68. DOI: 10.1118/1.598805. View

11.

Taylor P, Potts H . Computer aids and human second reading as interventions in screening mammography: two systematic reviews to compare effects on cancer detection and recall rate. Eur J Cancer. 2008; 44(6):798-807. DOI: 10.1016/j.ejca.2008.02.016. View

12.

Conant E, Toledano A, Periaswamy S, Fotin S, Go J, Boatsman J . Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis. Radiol Artif Intell. 2020; 1(4):e180096. PMC: 6677281. DOI: 10.1148/ryai.2019180096. View

13.

Hinton G, Osindero S, Teh Y . A fast learning algorithm for deep belief nets. Neural Comput. 2006; 18(7):1527-54. DOI: 10.1162/neco.2006.18.7.1527. View

14.

Badano A, Graff C, Badal A, Sharma D, Zeng R, Samuelson F . Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial. JAMA Netw Open. 2019; 1(7):e185474. PMC: 6324392. DOI: 10.1001/jamanetworkopen.2018.5474. View

15.

Sahiner B, Chan H, Petrick N, Wei D, Helvie M, Adler D . Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging. 1996; 15(5):598-610. DOI: 10.1109/42.538937. View

16.

Chan H, Lo S, Sahiner B, Lam K, Helvie M . Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network. Med Phys. 1995; 22(10):1555-67. DOI: 10.1118/1.597428. View

17.

Chan H, Doi K, Galhotra S, Vyborny C, Macmahon H, Jokich P . Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. Med Phys. 1987; 14(4):538-48. DOI: 10.1118/1.596065. View

18.

Chan H, Hadjiiski L, Samala R . Computer-aided diagnosis in the era of deep learning. Med Phys. 2020; 47(5):e218-e227. PMC: 7293164. DOI: 10.1002/mp.13764. View

19.

Kyono T, Gilbert F, van der Schaar M . Improving Workflow Efficiency for Mammography Using Machine Learning. J Am Coll Radiol. 2019; 17(1 Pt A):56-63. DOI: 10.1016/j.jacr.2019.05.012. View

20.

Sahiner B, Pezeshk A, Hadjiiski L, Wang X, Drukker K, Cha K . Deep learning in medical imaging and radiation therapy. Med Phys. 2018; 46(1):e1-e36. PMC: 9560030. DOI: 10.1002/mp.13264. View