Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks.

Li, Hongwei; Reichert, Maximilian; Lin, Kanru; Tselousov, Nikita; Braren, Rickmer; Fu, Deliang; Schmid, Roland; Li, Ji; Menze, Bjoern; Shi, Kuangyu (2019). Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 2095-2098. IEEE 10.1109/EMBC.2019.8856745

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The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early differential diagnosis of pancreatic cysts, which are identified in up to 16% of normal subjects, and some of them may develop into PDAC. Pancreatic cysts have a large variation in size and shape, and the precise segmentation of them remains rather challenging, which restricts the computer-aided interpretation of CT images acquired for differential diagnosis. We propose a computer-aided framework for early differential diagnosis of pancreatic cysts without pre-segmenting the lesions using densely-connected convolutional networks (Dense-Net). The Dense-Net learns high-level features from whole abnormal pancreas and builds mappings between medical imaging appearance to different pathological types of pancreatic cysts. To enhance the clinical applicability, we integrate saliency maps in the framework to assist the physicians to understand the decision of the deep learning method. The test on a cohort of 206 patients with 4 pathologically confirmed subtypes of pancreatic cysts has achieved an overall accuracy of 72.8%, which is significantly higher than the baseline accuracy of 48.1%. The superior performance on this challenging dataset strongly supports the clinical potential of our developed method.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Clinic of Nuclear Medicine

UniBE Contributor:

Shi, Kuangyu

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2694-0604

Publisher:

IEEE

Language:

English

Submitter:

Sabine Lanz

Date Deposited:

05 Jan 2021 17:31

Last Modified:

05 Jan 2021 17:31

Publisher DOI:

10.1109/EMBC.2019.8856745

PubMed ID:

31946314

BORIS DOI:

10.48350/149662

URI:

https://boris.unibe.ch/id/eprint/149662

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