Learning to Detect Malignant Breast Lesions from Partially Annotated Mammograms

核心概念

A novel two-stage training method to detect malignant breast lesions from mammograms with incomplete annotations, leveraging both fully and weakly annotated data.

要約

The paper proposes a new method called BRAIxDet to address the problem of detecting malignant breast lesions from mammograms with incomplete annotations. The dataset typically contains a subset of fully annotated images with lesion localization and classification, and another subset of weakly annotated images with only global classification labels.

The proposed approach has two main stages:

Pre-training Stage:

• The authors first pre-train a multi-view mammogram classifier called BRAIxMVCCL on the weakly annotated dataset and the weakly labeled version of the fully annotated dataset.
• This pre-training enables BRAIxMVCCL to produce a strong feature extractor and a GradCAM-based lesion detector.

Student-Teacher SSL Stage:

• The pre-trained BRAIxMVCCL model is then transformed into a detector called BRAIxDet, with a student-teacher semi-supervised learning approach.
• The student BRAIxDet is trained on the fully annotated dataset and the pseudo-labeled weakly annotated dataset, where the pseudo-labels are generated using the teacher's detections and the GradCAM detections from the pre-trained classifier.
• The teacher BRAIxDet is trained using an exponential moving average (EMA) of the student's parameters, with the batch normalization statistics frozen after pre-training to mitigate issues related to the dependency on the student's training samples and the mismatch between student and teacher parameters.

The authors provide extensive experiments on two real-world mammogram datasets with incomplete annotations, demonstrating state-of-the-art performance in lesion detection.

統計

"Breast cancer is the most commonly diagnosed cancer worldwide and the leading cause of cancer-related death in women."
"Real-world screening mammogram datasets commonly have a subset that is fully annotated and another subset that is weakly annotated with just the global classification."
"The training set has 7,532 weakly annotated cancer cases and 6,892 fully annotated cancer cases" in the ADMANI dataset.

引用

"Given the large size of such datasets, researchers usually face a dilemma with the weakly annotated subset: to not use it or to fully annotate it. The first option will reduce detection accuracy because it does not use the whole dataset, and the second option is too expensive given that the annotation needs to be done by expert radiologists."
"Our proposed BRAIxDet model achieves state-of-the-art (SOTA) performance on both datasets in terms of lesion detection measures."

抽出されたキーインサイト

BRAIxDet

by Yuanhong Che... 場所 arxiv.org 04-02-2024

https://arxiv.org/pdf/2301.13418.pdf

深掘り質問

How can the proposed approach be extended to handle other types of medical imaging data with incomplete annotations, such as chest X-rays or brain MRI scans

The proposed approach can be extended to handle other types of medical imaging data with incomplete annotations by adapting the methodology to suit the specific characteristics of the new imaging modality. For instance, in the case of chest X-rays or brain MRI scans, the following modifications can be made:

Feature Extraction: Modify the feature extraction process to capture the unique characteristics of chest X-rays or brain MRI scans. This may involve using different pre-trained models or designing specific architectures tailored to these modalities.

Lesion Detection: Adjust the lesion detection algorithms to account for the specific types of abnormalities present in chest X-rays or brain MRI scans. This may involve incorporating domain-specific knowledge or features to improve detection accuracy.

Data Augmentation: Implement data augmentation techniques that are relevant to chest X-rays or brain MRI scans, considering the specific variations and noise patterns present in these types of images.

Model Training: Fine-tune the training process to optimize the detection performance for the new imaging modality, considering the nuances and challenges associated with chest X-rays or brain MRI scans.

By customizing the approach to the characteristics of chest X-rays or brain MRI scans, the methodology can be effectively extended to handle incomplete annotations in these types of medical imaging data.

What are the potential limitations of the student-teacher semi-supervised learning approach, and how can it be further improved to handle more challenging cases of incomplete annotations

The student-teacher semi-supervised learning approach, while effective, may have some limitations that can be addressed for further improvement:

Confirmation Bias: One potential limitation is the risk of confirmation bias, where the student network may overfit to incorrect pseudo-labels provided by the teacher. This can lead to a decrease in detection accuracy. To mitigate this, additional mechanisms such as ensemble methods or more sophisticated pseudo-labeling strategies can be implemented.

Generalization: Ensuring the generalization of the model to diverse datasets with varying levels of incomplete annotations can be challenging. Techniques like domain adaptation or transfer learning can be employed to enhance the model's ability to generalize across different datasets.

Complexity: The student-teacher framework may introduce additional complexity to the training process, requiring careful tuning of hyperparameters and model architectures. Simplifying the approach while maintaining performance is crucial for practical implementation.

To further improve the student-teacher semi-supervised learning approach, researchers can explore advanced regularization techniques, ensemble methods, and data augmentation strategies tailored to the specific challenges of handling incomplete annotations in medical imaging data.

Given the importance of early breast cancer detection, how can the insights from this work be leveraged to develop more accessible and affordable breast cancer screening solutions, especially in resource-constrained regions

The insights from this work on early breast cancer detection can be leveraged to develop more accessible and affordable breast cancer screening solutions, especially in resource-constrained regions, through the following strategies:

Telemedicine and Remote Screening: Implement telemedicine solutions that allow for remote screening and consultation, enabling individuals in remote or underserved areas to access breast cancer screening services without the need for physical visits to healthcare facilities.

Mobile Health Applications: Develop mobile health applications that provide educational resources, self-assessment tools, and reminders for breast cancer screening appointments. These apps can empower individuals to take charge of their health and adhere to screening guidelines.

Community Outreach Programs: Collaborate with community organizations and local health authorities to organize breast cancer screening camps and awareness campaigns in underserved areas. These programs can increase awareness, facilitate early detection, and provide support to individuals in need.

Low-Cost Screening Technologies: Invest in the development of low-cost screening technologies, such as portable mammography units or AI-powered screening tools, that can be deployed in resource-constrained settings to improve access to early detection services.

By implementing these strategies and leveraging the insights from advanced breast cancer detection research, it is possible to enhance the accessibility and affordability of breast cancer screening services, ultimately leading to improved outcomes for individuals in resource-constrained regions.