Automated classification of liver lesions in multi-phase CT and MR scans is
of clinical significance but challenging. This study proposes a novel Siamese
Dual-Resolution Transformer (SDR-Former) framework, specifically designed for
liver lesion classification in 3D multi-phase CT and MR imaging with varying
phase counts. The proposed SDR-Former utilizes a streamlined Siamese Neural
Network (SNN) to process multi-phase imaging inputs, possessing robust feature
representations while maintaining computational efficiency. The weight-sharing
feature of the SNN is further enriched by a hybrid Dual-Resolution Transformer
(DR-Former), comprising a 3D Convolutional Neural Network (CNN) and a tailored
3D Transformer for processing high- and low-resolution images, respectively.
This hybrid sub-architecture excels in capturing detailed local features and
understanding global contextual information, thereby, boosting the SNN's
feature extraction capabilities. Additionally, a novel Adaptive Phase Selection
Module (APSM) is introduced, promoting phase-specific intercommunication and
dynamically adjusting each phase's influence on the diagnostic outcome. The
proposed SDR-Former framework has been validated through comprehensive
experiments on two clinical datasets: a three-phase CT dataset and an
eight-phase MR dataset. The experimental results affirm the efficacy of the
proposed framework. To support the scientific community, we are releasing our
extensive multi-phase MR dataset for liver lesion analysis to the public. This
pioneering dataset, being the first publicly available multi-phase MR dataset
in this field, also underpins the MICCAI LLD-MMRI Challenge. The dataset is
accessible at:this https URL

提出了一种新的 Siamese Dual-Resolution Transformer (SDR-Former) 框架，特别针对不同相数的 3D 多相 CT 和 MR 成像中的肝脏病变分类，利用流体动力学 Siamese Neural Network (SNN) 处理多相成像输入，通过混合的 Dual-Resolution Transformer (DR-Former) 子架构以及新颖的 Adaptive Phase Selection Module (APSM) 实现特征提取和相特定的相互通信，从而提高了病变分类的准确性。