Parameterized Hypercomplex Attention Maps for Efficient Breast Cancer Classification

المفاهيم الأساسية

A novel framework that leverages attention maps and parameterized hypercomplex neural networks to efficiently classify breast cancer in mammography and histopathological images.

الملخص

The proposed framework, called Parameterized Hypercomplex Attention Maps (PHAM), consists of two key components:

Attention-map augmentation: The framework computes attention maps for each input image using a pre-trained PatchConvNet model. These attention maps are then used to condition the training of the classification model, allowing it to focus on the most critical regions of the image.

Parameterized hypercomplex neural networks: The backbone of the PHAM framework is a parameterized hypercomplex neural network (PHResNet), which can effectively model the relations between the original breast cancer images and the corresponding attention maps. The hypercomplex algebra properties enable the network to capture local and global relations in the multi-dimensional input, leading to improved performance compared to real-valued counterparts.

The authors validate the PHAM framework on publicly available datasets of mammography (CBIS-DDSM and INbreast) and histopathological images (BreakHis). The results demonstrate that the proposed approach outperforms attention-based state-of-the-art methods and the real-valued version of the framework, while using significantly fewer parameters.

الإحصائيات

The CBIS-DDSM dataset contains 991 images for training, 240 for validation, and 361 for testing.
The INbreast dataset contains 410 full-field digital mammography images, with 20% used for validation and testing, respectively.
The BreakHis dataset contains 9,109 microscopic images of breast tissue at four different magnifying factors, with 20% used for validation and testing.

اقتباسات

"Attention maps provide critical information regarding the ROI and allow the neural model to concentrate on it."
"The hypercomplex architecture has the ability to model local relations between input dimensions thanks to hypercomplex algebra rules, thus properly exploiting the information provided by the attention map."

الرؤى الأساسية المستخلصة من

Attention-Map Augmentation for Hypercomplex Breast Cancer Classification

by Eleonora Lop... في arxiv.org 04-24-2024

https://arxiv.org/pdf/2310.07633.pdf

استفسارات أعمق

How can the PHAM framework be extended to handle multi-modal medical imaging data, such as combining mammography and ultrasound images for breast cancer diagnosis

To extend the PHAM framework to handle multi-modal medical imaging data, such as combining mammography and ultrasound images for breast cancer diagnosis, a few key steps can be taken:

Data Fusion: The first step would involve integrating the information from different modalities, such as mammography and ultrasound images. This can be achieved by combining the features extracted from each modality using techniques like concatenation or fusion layers.

Attention Maps for Each Modality: Attention maps can be generated for each modality separately to highlight the most relevant regions in each image. This would involve adapting the attention mechanism to work with multi-modal data.

Parameterized Hypercomplex Networks: The multi-dimensional input for the PHAM framework would now include the original images from both modalities along with their respective attention maps. The hypercomplex neural network architecture would need to be adjusted to handle this multi-modal input.

Training and Evaluation: The framework would then be trained on the combined dataset, leveraging the attention maps to focus on important regions in both types of images. Evaluation would involve testing the model on unseen multi-modal data to assess its performance in diagnosing breast cancer using a combination of mammography and ultrasound images.

By extending the PHAM framework in this manner, it can effectively leverage the complementary information provided by different imaging modalities to improve the accuracy and reliability of breast cancer diagnosis.

What other medical imaging tasks could benefit from the integration of attention maps and parameterized hypercomplex neural networks

Several other medical imaging tasks could benefit from the integration of attention maps and parameterized hypercomplex neural networks. Some examples include:

Brain Tumor Segmentation: Attention maps can help highlight tumor regions in MRI scans, guiding the neural network to focus on critical areas for accurate segmentation. Hypercomplex networks can then capture complex relationships within the multi-dimensional MRI data.

Pulmonary Disease Classification: In chest X-rays or CT scans, attention maps can identify areas indicative of pulmonary diseases. By incorporating these maps into a hypercomplex network, the model can better understand the spatial relationships within the images for improved classification.

Cardiac Image Analysis: Attention maps can pinpoint regions of interest in echocardiograms or cardiac MRI scans, aiding in the detection of abnormalities. Hypercomplex networks can then analyze the complex interactions within the multi-dimensional cardiac images for precise diagnosis.

Bone Fracture Detection: Attention maps can highlight potential fracture sites in X-ray images, assisting in fracture detection. Hypercomplex networks can leverage this information to capture intricate patterns in the bone structures for accurate classification.

By integrating attention maps and parameterized hypercomplex neural networks, these medical imaging tasks can benefit from enhanced interpretability, improved feature extraction, and better performance in disease detection and diagnosis.

Can the PHAM framework be adapted to address the challenge of class imbalance in medical datasets, where the number of positive and negative samples may be significantly different

Adapting the PHAM framework to address class imbalance in medical datasets, where the number of positive and negative samples may be significantly different, can be achieved through the following strategies:

Attention Weighting: Assigning different weights to the attention maps based on the class distribution can help the model focus more on underrepresented classes during training. This can be implemented by adjusting the attention mechanism to prioritize regions in minority class samples.

Oversampling and Undersampling: Augmenting the dataset through oversampling of minority class samples and undersampling of majority class samples can balance the class distribution. Attention maps can then be used to guide the model's focus on the augmented data.

Class-Weighted Loss Functions: Introducing class-weighted loss functions can penalize misclassifications in the minority class more heavily, encouraging the model to learn from these instances. Attention maps can assist in emphasizing important regions for correct classification.

Ensemble Techniques: Utilizing ensemble methods with multiple models trained on different class-balanced subsets of the data can help mitigate class imbalance issues. Attention maps can guide the fusion of predictions from diverse models to improve overall performance.

By incorporating these strategies into the PHAM framework, it can be adapted to handle class imbalance in medical datasets, ensuring robust and accurate breast cancer classification even in scenarios with imbalanced class distributions.