A multi-task fusion model based on a residual-Multi-layer perceptron network for mammographic breast cancer screening

BACKGROUND AND OBJECTIVE

Deep learning approaches are being increasingly applied for medical computer-aided diagnosis (CAD). However, these methods generally target only specific image-processing tasks, such as lesion segmentation or benign state prediction. For the breast cancer screening task, single feature extraction models are generally used, which directly extract only those potential features from the input mammogram that are relevant to the target task. This can lead to the neglect of other important morphological features of the lesion as well as other auxiliary information from the internal breast tissue. To obtain more comprehensive and objective diagnostic results, in this study, we developed a multi-task fusion model that combines multiple specific tasks for CAD of mammograms.

METHODS

We first trained a set of separate, task-specific models, including a density classification model, a mass segmentation model, and a lesion benignity-malignancy classification model, and then developed a multi-task fusion model that incorporates all of the mammographic features from these different tasks to yield comprehensive and refined prediction results for breast cancer diagnosis.

RESULTS

The experimental results showed that our proposed multi-task fusion model outperformed other related state-of-the-art models in both breast cancer screening tasks in the publicly available datasets CBIS-DDSM and INbreast, achieving a competitive screening performance with area-under-the-curve scores of 0.92 and 0.95, respectively.

CONCLUSIONS

Our model not only allows an overall assessment of lesion types in mammography but also provides intermediate results related to radiological features and potential cancer risk factors, indicating its potential to offer comprehensive workflow support to radiologists.

YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms

BACKGROUND AND OBJECTIVE

Both mass detection and segmentation in digital mammograms play a crucial role in early breast cancer detection and treatment. Furthermore, clinical experience has shown that they are the upstream tasks of pathological classification of breast lesions. Recent advancements in deep learning have made the analyses faster and more accurate. This study aims to develop a deep learning model architecture for breast cancer mass detection and segmentation using the mammography.

METHODS

In this work we proposed a double shot model for mass detection and segmentation simultaneously using a combination of YOLO (You Only Look Once) and LOGO (Local-Global) architectures. Firstly, we adopted YoloV5L6, the state-of-the-art object detection model, to position and crop the breast mass in mammograms with a high resolution; Secondly, to balance training efficiency and segmentation performance, we modified the LOGO training strategy to train the whole images and cropped images on the global and local transformer branches separately. The two branches were then merged to form the final segmentation decision.

RESULTS

The proposed YOLO-LOGO model was tested on two independent mammography datasets (CBIS-DDSM and INBreast). The proposed model performs significantly better than previous works. It achieves true positive rate 95.7% and mean average precision 65.0% for mass detection on CBIS-DDSM dataset. Its performance for mass segmentation on CBIS-DDSM dataset is F1-score=74.5% and IoU=64.0%. The similar performance trend is observed in another independent dataset INBreast as well.

CONCLUSIONS

The proposed model has a higher efficiency and better performance, reduces computational requirements, and improves the versatility and accuracy of computer-aided breast cancer diagnosis. Hence it has the potential to enable more assistance for doctors in early breast cancer detection and treatment, thereby reducing mortality.

Mammogram mass segmentation and detection using Legendre neural network-based optimal threshold

Breast cancer is a leading cause of mortality affecting women across the world. Early detection and diagnosis can decrease the mortality rate due to this cancer. Machine learning-based models are gaining popularity for biomedical applications due to the ability of nonlinear mapping between input and output patterns using supervised training phase. The research work in the paper is focused on the optimal adaptive threshold for mammogram mass segmentation, and detection in order to assist radiologist in accurate diagnosis Legendre neural network with single layer is used to develop the model, and the training is performed through Block Based Normalized Sign-Sign Least Mean Square (BBNSSLMS) algorithm. Legendre neural network expands the input vector using standard Legendre polynomial, and the recursive update principle is followed for the weight vector in higher dimension. The optimal threshold is indirectly used for proper segmentation of mammogram mass. The proposed segmentation method involves training phase with 30 images and testing phase by 151 images obtained from standard Mammogram Image Analysis Society (MIAS) database. The proposed model achieved a sensitivity of 95% and accuracy of 96% with false positives per image calculated as 1.19. • Threshold selection is carried out using single-layer Legendre NN with reduced computational complexity. BNSSLMS algorithm process the data samples block wise instead of sample by sample basis. Optimal threshold is generated according to the varying image properties which helps in correct segmentation and detection. • Due to sparse nature of the adaptive model, more numbers of weight coefficients are tending to zero which also helps in faster convergence. Mammogram Mass Detection Steps.

Mass segmentation of mammograms using Markov models associated with constrained clustering

In this paper, we propose four variants of the Markov random field model by using constrained clustering for breast mass segmentation. These variants were tested with a set of images extracted from a public database. The obtained results have shown that the proposed variants, which allow to include additional information in the form of constraints to the clustering process, present better visual segmentation results than the original model, as well as a lower final energy which implies a better quality in the final segmentation. Specifically, the centroid initialization method used by our variants allows us to locate about 90% of the regions of interest that contain a mass, which subsequently with the pairwise constraints helped us recover a maximum of 93% of the masses. The segmentation results are also quantitatively evaluated using three supervised segmentation measures. These measures show that the mass segmentation quality of the proposed variants, considering the breast density level, is consistent with the corresponding segmentation annotated by specialized radiologists.

Mammographic mass segmentation using fuzzy contours

BACKGROUND AND OBJECTIVE

Accurate mass segmentation in mammographic images is a critical requirement for computer-aided diagnosis systems since it allows accurate feature extraction and thus improves classification precision.

METHODS

In this paper, a novel automatic breast mass segmentation approach is presented. This approach consists of mainly three stages: contour initialization applied to a given region of interest; construction of fuzzy contours and estimation of fuzzy membership maps of different classes in the considered image; integration of these maps in the Chan-Vese model to get a fuzzy-energy based model that is used for final delineation of mass.

RESULTS

The proposed approach is evaluated using mass regions of interest extracted from the mini-MIAS database. The experimental results show that the proposed method achieves an average true positive rate of 91.12% with a precision of 88.08%.

CONCLUSIONS

The achieved results show high accuracy in breast mass segmentation when compared to manually annotated ground truth and to other methods from the literature.