Integration of Slice-Based Diagnostic Probabilities Predicted by 2D Deep Learning Using ResNet50 to Yield Lesion-Based Diagnosis in Breast MRI
Yang Zhang, Jiejie Zhou, Zhongwei Chen, Youfan Zhao, Meihao Wang, Yan-Lin Liu, Jeon-Hor Chen, Ke Nie, Min‐Ying Su · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024
A deep learning model using 2D ResNet50 CNN was trained to differentiate a dataset of 103 malignant vs 73 benign breast lesions, then tested in a testing dataset of 53 malignant and 31 benign cases. The 2D slice-based results were used to calculate a probability for each lesion, by using 5 methods: (1) slice-based average, (2) tumor area weighted average, (3) tumor perimeter weighted average, (4) using the probability of the largest tumor slice, (5) using the highest probability among all slices. The results showed using the highest probability to convert from slice-based to lesion-based diagnosis had the best performance.