Screening mammography is the most widely used method for early breast cancer
detection, significantly reducing mortality rates. The integration of
information from multi-view mammograms enhances radiologists' confidence and
diminishes false-positive rates since they can examine on dual-view of the same
breast to cross-reference the existence and location of the lesion. Inspired by
this, we present TransReg, a Computer-Aided Detection (CAD) system designed to
exploit the relationship between craniocaudal (CC), and mediolateral oblique
(MLO) views. The system includes cross-transformer to model the relationship
between the region of interest (RoIs) extracted by siamese Faster RCNN network
for mass detection problems. Our work is the first time cross-transformer has
been integrated into an object detection framework to model the relation
between ipsilateral views. Our experimental evaluation on DDSM and VinDr-Mammo
datasets shows that our TransReg, equipped with SwinT as a feature extractor
achieves state-of-the-art performance. Specifically, at the false positive rate
per image at 0.5, TransReg using SwinT gets a recall at 83.3% for DDSM dataset
and 79.7% for VinDr-Mammo dataset. Furthermore, we conduct a comprehensive
analysis to demonstrate that cross-transformer can function as an
auto-registration module, aligning the masses in dual-view and utilizing this
information to inform final predictions. It is a replication diagnostic
workflow of expert radiologists

利用多视角乳房 X 光照片的信息，利用交互变换器建模同侧视图之间的关系来检测和预测乳房肿块，取得了令人满意的效果。

TransReg：多视图乳腺 X 线检测中的交叉变换器作为自动注册模块

TransReg: Cross-transformer as auto-registration module for multi-view  mammogram mass detection

Few-shot object detection (FSOD), with the aim to detect novel objects using
very few training examples, has recently attracted great research interest in
the community. Metric-learning based methods have been demonstrated to be
effective for this task using a two-branch based siamese network, and calculate
the similarity between image regions and few-shot examples for detection.
However, in previous works, the interaction between the two branches is only
restricted in the detection head, while leaving the remaining hundreds of
layers for separate feature extraction. Inspired by the recent work on vision
transformers and vision-language transformers, we propose a novel Fully
Cross-Transformer based model (FCT) for FSOD by incorporating cross-transformer
into both the feature backbone and detection head. The asymmetric-batched
cross-attention is proposed to aggregate the key information from the two
branches with different batch sizes. Our model can improve the few-shot
similarity learning between the two branches by introducing the multi-level
interactions. Comprehensive experiments on both PASCAL VOC and MSCOCO FSOD
benchmarks demonstrate the effectiveness of our model.

本文提出了一种新的基于 Fully Cross-Transformer 的模型 (FCT) 实现 few-shot 目标检测，通过在特征 backbone 和检测头中结合跨模块交互 attention，提高模型中不同表示层之间的相似性学习以达到更好的性能。在 PASCAL VOC 和 MSCOCO FSOD 基准测试上的实验验证了该模型的有效性。