Single-domain generalized breast tumor detection in X-ray images
Objective Breast tumor detection in X-ray images is a great challenge in the domain of medical image analysis,primarily because of the intrinsic difficulty in discerning lesions due to their significant concealment and propensity for metastasis.Currently,computer-aided diagnosis(CAD)plays a pivotal role in early tumor detection and diagnosis.Remarkable progress has been achieved in detecting breast tumors in X-ray images through deep learning-based object detection methods when the training and testing data are of the same modality.However,the limited availability of medical image data and the labor-intensive and professional nature of data annotation have constrained the detection performance and generalization ability of models.In addition,the presence of domain shift in the unseen domains caused by noise impairs the performance of breast tumor detection across diverse environments.To address these issues,existing studies have proposed different methods,including domain adaptation and domain generalization.However,domain adaptation requires a partition between the target and source domains,while domain generalization requires training the models in mul-tiple domains.Achieving domain division poses a formidable challenge due to the limited availability of medical data.Therefore,in response to these challenges,single-domain methods have been proposed to train the models in a single domain and then they are generalized to the unseen domains in recent years.These methods are well-suited for medical data for aiding in mitigating domain shifts.Though single-domain generalization has been widely applied in classification tasks,its application to object detection tasks remains relatively nascent due to the inherent differences between object detection and classification.Through analysis,we found the single instance only focuses on holistic images for domain alignment in the classification tasks.In contrast,object detection tasks entail the simultaneous consideration of multiple objects within each image,which leads to the mismatch of instances.Thus,we propose a novel instance alignment para-digm to facilitate the single-domain generalization for detecting breast tumors.Method To improve the generalization perfor-mance for robust breast tumor detection in X-ray images,we propose a novel model called the single-domain generalization model(SDGM).The SDGM is constructed upon the baseline(RetinaNet)and employs Resnet-50 as its backbone.Two pivotal modules,namely,the instance generalization module(IGM)and the domain feature enhancement module(DFEM),are developed.First,the IGM is strategically positioned at the detection head to enhance the generalization per-formance by normalizing and whitening the category semantic information of each instance.The IGM comprises N sets of 3×3 convolutions and the switchable whitening sub-module,which is widely recognized for its effectiveness in extracting instance domain-invariant features in classification tasks.Therefore,IGM is integrated into the classification branch at the detection head.Second,the DFEM is ingeniously devised to efficiently merge the global information from both up-sampling and down-sampling processes while mitigating the impact of noise in medical images.To counteract the noise generated by conventional convolution in spatial features,a 3 × 3 convolution is employed to generate a foreground mask image,which serves as the convolution offset to guide the deformable convolution for sampling.Subsequently,channel-wise attention is leveraged to selectively suppress noise within each channel.The DFEM is incorporated into the feature pyramid network to attenuate the noise during the fusion of feature maps at various scales,thereby promoting subsequent domain-invariant fea-ture extraction.Result To assess the efficiency of our proposed SDGM,we conduct extensive experiments on the CBIS-DDSM dataset and the INbreast dataset,which is single-domain generalized with multiple domains in the intra-domain.Additionally,we compare the SDGM against several state-of-the-art methods.We also evaluate the inter-domain generaliza-tion performance between the CBIS-DDSM and INbreast datasets.In the intra-domain single-domain generalization sce-narios,the SDGM consistently outperforms the baseline method(RetinaNet)by a 9.7％increase in mean average preci-sion.Furthermore,it surpasses other one-stage anchor-free methods(e.g.,FCOS and FoveaBox),one-stage anchor-based methods(e.g.,ATSS and TOOD),two-stage methods(e.g.,Faster R-CNN and Cascade-RCNN),and even the transformer-based method PVTv2.In the supervised learning scenarios,the SDGM trained with only 728 images,sur-passes RetinaNet,Cascade-RCNN,FoveaBox,and FCOS trained with 5 148 images.This result demonstrates that the SDGM exhibits remarkable generalization capabilities,outperforming supervised methods with substantially less training data.Furthermore,we assess the impact of the attention mechanism on the model performance.Compared with the method TOOD without attention,the SDGM alleviates domain shift to achieve at least a 3.6％improvement in the single-domain generalization scenario.Additionally,compared with PVTv2 and ResNeSt,which employ different attention mechanisms,the SDGM alleviates domain shift to achieve 21.1％and 2.8％improvement respectively,in the single-domain generaliza-tion scenarios.In the inter-domain single-domain generalization scenarios,the SDGM displays a performance improvement of 5.8％compared with the baseline.These results indicate that our proposed SDGM not only mitigates performance degra-dation but also has robustness and generalization capabilities across different datasets.Conclusion In this study,we develop the SDGM for detecting breast tumors in X-ray images and focus on designing two important components:the DFEM and the IGM.The DFEM improves the performance of SDGM by effectively suppressing the noise in the global infor-mation.Meanwhile,the IGM is positioned at the detection head to enhance the generalization ability by normalizing and whitening the category information for each object.We evaluate the SDGM on the INbreast and CBIS-DDSM datasets with multiple benchmarks to evaluate its efficiency.The SDGM can handle domain shift and perform well even with limited labeled medical data,mitigating challenges in medical image analysis.Additionally,the SDGM exhibits robustness across different environmental conditions.In summary,the SDGM offers a promising solution to improving breast tumor detection in X-ray images,making a valuable impact on clinical practice.
breast tumor detection in X-ray imagessingle-domain generalizationdomain shiftnormalization and whit-eningfeature enhancement
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