A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture.

Journal: Journal of X-ray science and technology

PMID: 39973786

Abstract

OBJECTIVE: Early diagnosis of pancreatic cystic neoplasm (PCN) is crucial for patient survival. This study proposes M-YOLO, a novel model combining Mamba architecture and YOLO, to enhance the detection of pancreatic cystic tumors. The model addresses the technical challenge posed by the tumors' complex morphological features in medical images.

Authors

Junlong Dai

School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Cong He

Department of Radiology, Shaoxing Second Hospital, 123 Yanan Rd, Shaoxing, 312000, Zhejiang, China.
Liang Jin

Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.
Chengwei Chen

Department of Radiology, Changhai Hospital.
Jie Wu

Center of Disease Control of Qingdao, 175 Shandong Road, Qingdao, Shandong, 266001, China.
Yun Bian

Department of Radiology, Changhai Hospital.

Keywords

Algorithms Deep Learning Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Pancreatic Cyst Pancreatic Neoplasms Sensitivity and Specificity Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (39973786)

A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture.

Abstract

Authors

Keywords

External Resources

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