Mammograms For Detection Research Articles

Factors affecting mammogram breast cancer surveillance effectiveness in the ipsilateral and contralateral breast

AimMammography is the mainstay of imaging surveillance after breast cancer (BC) treatment, but false negatives can occur. The objective of the study was to determine the factors that can predict poorer second breast cancer (SBC) mammogram detection of the ipsilateral and contralateral breast separately. MethodsA multicentre retrospective review was performed on female patients with a previous history of treated BC who developed a second breast cancer (SBC) in the ipsilateral (ISBC) or contralateral breast (CSBC) within 10 years from the first BC. SBC cases that occurred between January 2006 and October 2017 were included from the institutional database. The ISBC and CSBC mammogram-occult (MO) rates were correlated with mammographic breast density as well as various clinical, radiological and histological characteristics of the first BC. Results274 cases of SBC were evaluated. 39.4 % (108/274) of cases were ISBC and 60.6 % (166/274) were CSBC. 35 (32.4 %) of the ISBCs and 42 (25.3 %) of the CSBCs were MO (p = 0.218). On multivariate analysis, symptomatic first BC (p = 0.041), prevailing dense breast tissue at the time of SBC diagnosis (p = 0.003) and trabecular thickening on surveillance mammograms (p = 0.017) were associated with MO ISBC. MO first BC (p < 0.001) was the only factor found to correlate with MO CSBC. ConclusionThe study found various clinical, radiological and pathological factors associated with mammogram surveillance failure for the ipsilateral and contralateral breast. This information can provide additional guidance in the planning of a personalised surveillance program using adjunct imaging screening.

Improved PAA algorithm for breast mass detection in mammograms

Mammography screening is instrumental in the early detection and diagnosis of breast cancer by identifying masses in mammograms. With the rapid development of deep learning, numerous deep learning-based object detection algorithms have been explored for mass detection studies. However, these methods often yield a high false positive rate per image (FPPI) while achieving a high true positive rate (TPR). To maintain a higher TPR while also ensuring lower FPPI, we improved the Probability Anchor Assignment (PAA) algorithm to enhance the detection capability for mammographic characteristics with our previous work. We considered three dimensions: the backbone network, feature fusion module, and dense detection heads. The final experiment showed the effectiveness of the proposed method, and the TPR/FPPI values of the final improved PAA algorithm were 0.96/0.56 on the INbreast datasets. Compared to other methods, our method stands distinguished with its effectiveness in addressing the imbalance between positive and negative classes in cases of single lesion detection.

Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion remover

BackgroundThe wide heterogeneity in the appearance of breast lesions and normal breast structures can confuse computerized detection algorithms. Our purpose was therefore to develop a Lesion Highlighter (LH) that can improve the performance of computer-aided detection algorithms for detecting breast cancer on screening mammograms.MethodsWe hypothesized that a Cycle-GAN based Lesion Remover (LR) could act as an LH, which can improve the performance of lesion detection algorithms. We used 10,310 screening mammograms from 4,832 women that included 4,942 recalled lesions (BI-RADS 0) and 5,368 normal results (BI-RADS 1). We divided the dataset into Train:Validate:Test folds with the ratios of 0.64:0.16:0.2. We segmented image patches (400 × 400 pixels) from either lesions marked by MQSA radiologists or normal tissue in mammograms. We trained a Cycle-GAN to develop two GANs, where each GAN transferred the style of one image to another. We refer to the GAN transferring the style of a lesion to normal breast tissue as the LR. We then highlighted the lesion by color-fusing the mammogram after applying the LR to its original. Using ResNet18, DenseNet201, EfficientNetV2, and Vision Transformer as backbone architectures, we trained three deep networks for each architecture, one trained on lesion highlighted mammograms (Highlighted), another trained on the original mammograms (Baseline), and Highlighted and Baseline combined (Combined). We conducted ROC analysis for the three versions of each deep network on the test set.ResultsThe Combined version of all networks achieved AUCs ranging from 0.963 to 0.974 for identifying the image with a recalled lesion from a normal breast tissue image, which was statistically improved (p-value < 0.001) over their Baseline versions with AUCs that ranged from 0.914 to 0.967.ConclusionsOur results showed that a Cycle-GAN based LR is effective for enhancing lesion conspicuity and this can improve the performance of a detection algorithm.

MAMMOGRAM DETECTION USING MACHINE LEARNING

Mammogram detection plays a critical role in early breast cancer diagnosis, and leveraging machine learning techniques can significantly enhance diagnostic accuracy. This project addresses the classification and segmentation of mammogram images to identify abnormalities. We employed a Convolutional Neural Network (CNN) algorithm for binary classification to categorize mammogram images as either ‘Normal’ or ‘Malignant’. For region-based segmentation of detected abnormalities, we utilized an Attention-based optimized UNET algorithm. The preprocessing phase included resizing, normalization, and shuffling of the dataset to prepare it for training. Due to the large size of comprehensive mask image datasets (166 GB), which are impractical to download or process with standard systems, we used a smaller subset of images for training. Consequently, while the classification model performs with reasonable accuracy, the segmentation results may exhibit limited precision due to the constrained training data. The trained model can be applied to test images, where malignant predictions trigger segmentation of the affected regions using the UNET algorithm. This approach demonstrates the feasibility of integrating CNN and UNET for mammogram analysis, though future work will benefit from access to larger datasets for improved segmentation accuracy.

Revolutionizing Breast Cancer Detection: Enhanced Mammogram Analysis with Modified Xception and Self-Attention

In the realm of precision medicine, the intersection of state-of-the-art technology and disease identification has ushered in a new era of accuracy in healthcare diagnostics. This paper reviews a groundbreaking research endeavor aiming to enhance breast cancer detection in mammograms through the synergistic application of transfer learning, architectural modifications, and advanced optimization techniques.&#x0D; At the core of this research is the concept of transfer learning, leveraging insights gained from one domain to illuminate another. The study extends this paradigm to the Xception architecture, renowned for its proficiency in discerning intricate patterns within images. However, the novelty lies in the intelligent modification of Xception to be specifically attuned to breast cancer detection. By fine-tuning the network's final layers, the model's innate ability to understand features is harmonized with the complexities of mammographic images, ensuring sensitivity to nuanced markers of potential malignancy.&#x0D; A distinctive aspect of this research is the incorporation of a self-attention mechanism, mirroring human visual processing. This mechanism dynamically highlights crucial regions within mammograms, transforming the model into an active interpreter capable of identifying subtle textures, shapes, and edges indicative of breast cancer. This adaptive approach enhances the model's finesse in navigating diverse breast cancer cases effectively.&#x0D; Throughout the training phase, optimization is pivotal for steering the research towards success. The utilization of the Adam optimizer, known for its adaptability in learning rates and moment estimations, guides the process, ensuring precise gradient descent through complex patterns. The integration of the rectified linear unit (ReLU) activation function further empowers the model to capture intricate relationships within data, enhancing its ability to identify subtle cancer markers. This review comprehensively explores the innovative strides made in breast cancer detection, shedding light on the nuanced interplay of transfer learning, architectural adjustments, self-attention mechanisms, and advanced optimization techniques. The modified Xception model emerges as a promising tool in the pursuit of accurate and sensitive breast cancer diagnostics.

CalAttnMix: An instance-attention based data augmentation method for calcification detection in mammograms

Data augmentation is widely used as a pre-processing step to solve limited training data and class imbalance problems in deep-learning based calcification detection methods. However, current augmentation methods in breast mammograms fail to guarantee the class balance and diversity of generated calcifications, especially that randomly fusing instances with specified regions results in unrealistic images, further degrading the performances. Here, we propose an instance-attention based data augmentation method, named Calcification-Attention-Mix (CalAttnMix), which adaptively transfers multiple source calcification instances into diverse breast tissue backgrounds, aiding breast calcification detection. First, to seek feasible breast tissue candidate backgrounds, an Augmentation Region Proposal Mechanism (ARPM) is devised based on morphology constraints of calcifications. Then, without segmentation masks of calcification foregrounds, we design a transformer-based Foreground Prediction Module (FPM), along with a multi-task learning strategy, to localize instances accurately in a weakly-supervised manner. Finally, to generate diverse instances, an attention mix module is introduced to dynamically mix instances with background candidates, simulating various breast tissue surroundings. By focusing on the minor class, CalAttnMix generates diverse and balanced datasets for calcification detection. Experiments on the public and clinical dataset show that CalAttnMix achieves the lowest FID 22.152 and cosine similarity score 0.304, respectively, confirming the realism and diversity of generated images. CalAttnMix outperforms SOTA augmentation methods by 3.40% / 2.30% and 1.40% / 5.00% in the average recall/mAP, demonstrating the best detection results.

A comparative study of YOLO models and a transformer-based YOLOv5 model for mass detection in mammograms

A comparative study of YOLO models and a transformer-based YOLOv5 model for mass detection in mammograms

A Yolo-Based Model for Breast Cancer Detection in Mammograms

This work aims to implement an automated data-driven model for breast cancer detection in mammograms to support physicians’ decision process within a breast cancer screening or detection program. The public available CBIS-DDSM and the INbreast datasets were used as sources to implement the transfer learning technique on full-field digital mammography proprietary dataset. The proprietary dataset reflects a real heterogeneous case study, consisting of 190 masses, 46 asymmetries, and 71 distortions. Several Yolo architectures were compared, including YoloV3, YoloV5, and YoloV5-Transformer. In addition, Eigen-CAM was implemented for model introspection and outputs explanation by highlighting all the suspicious regions of interest within the mammogram. The small YoloV5 model resulted in the best developed solution obtaining an mAP of 0.621 on proprietary dataset. The saliency maps computed via Eigen-CAM have proven capable solution reporting all regions of interest also on incorrect prediction scenarios. In particular, Eigen-CAM produces a substantial reduction in the incidence of false negatives, although accompanied by an increase in false positives. Despite the presence of hard-to-recognize anomalies such as asymmetries and distortions on the proprietary dataset, the trained model showed encouraging detection capabilities. The combination of Yolo predictions and the generated saliency maps represent two complementary outputs for the reduction of false negatives. Nevertheless, it is imperative to regard these outputs as qualitative tools that invariably necessitate clinical radiologic evaluation. In this view, the model represents a trusted predictive system to support cognitive and decision-making, encouraging its integration into real clinical practice.

Letter: The Urgency of Neurosurgical Leadership in the Era of Artificial Intelligence.

Letter: The Urgency of Neurosurgical Leadership in the Era of Artificial Intelligence.

Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms

The early detection and diagnosis of breast cancer may increase survival rates and reduce overall treatment costs. The cancer of the breast is a severe and potentially fatal disease that impacts individuals worldwide. Mammography is a widely utilized imaging technique for breast cancer surveillance and diagnosis. However, images produced with mammography frequently contain noise, poor contrast, and other anomalies that hinder radiologists from interpreting the images. This study develops a novel deep-learning technique for breast cancer detection using mammography images. The proposed procedure consists of two primary steps: region-of-interest (ROI) (1) extraction and (2) classification. At the beginning of the procedure, a YOLOX model is utilized to distinguish breast tissue from the background and to identify ROIs that may contain lesions. In the second phase, the EfficientNet or ConvNeXt model is applied to the data to identify benign or malignant ROIs. The proposed technique is validated using a large dataset of mammography images from various institutions and compared to several baseline methods. The pF1 index is used to measure the effectiveness of the technique, which aims to establish a balance between the number of false positives and false negatives, and is a harmonic mean of accuracy and recall. The proposed method outperformed existing methods by an average of 8.0%, obtaining superior levels of precision and sensitivity, and area under the receiver operating characteristics curve (ROC AUC) and the precision–recall curve (PR AUC). In addition, ablation research was conducted to investigate the effects of the procedure’s numerous components. According to the findings, the proposed technique is another choice that could enhance the detection and diagnosis of breast cancer using mammography images.

Signals of global symmetry are important for abnormality detection in mammograms.

Expert radiologists can detect the "gist of abnormal" in bilateral mammograms even three years prior to onset of cancer. However, their performance decreases if both breasts are not from the same woman, suggesting the ability to detect the abnormality is partly dependent on a global signal present across the two breasts. We aim to detect this implicitly perceived "symmetry" signal by examining its effect on a pre-trained mammography model. A deep neural network (DNN) with four mammogram view inputs was developed to predict whether the mammograms come from one woman, or two different women as the first step in investigating the symmetry signal. Mammograms were balanced by size, age, density, and machine type. We then evaluated a cancer detection DNN's performance on mammograms from the same and different women. Finally, we used textural analysis methods to further explain the symmetry signal. The developed DNN can detect whether a set of mammograms come from the same or different woman with a base accuracy of 61%. Indeed, a DNN shown mammograms swapped either contralateral or abnormal with a normal mammogram from another woman, resulted in performance decreases. Findings indicate that abnormalities induce a disruption in global mammogram structure resulting in the break in the critical symmetry signal. The global symmetry signal is a textural signal embedded in the parenchyma of bilateral mammograms, which can be extracted. The presence of abnormalities alters textural similarities between the left and right breasts and contributes to the "medical gist signal."

Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis.

Mass detection in mammograms has a limited approach to the presence of a mass in overlapping denser fibroglandular breast regions. In addition, various breast density levels could decrease the learning system's ability to extract sufficient feature descriptors and may result in lower accuracy performance. Therefore, this study is proposing a textural-based image enhancement technique named Spatial-based Breast Density Enhancement for Mass Detection (SbBDEM) to boost textural features of the overlapped mass region based on the breast density level. This approach determines the optimal exposure threshold of the images' lower contrast limit and optimizes the parameters by selecting the best intensity factor guided by the best Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE) scores separately for both dense and non-dense breast classes prior to training. Meanwhile, a modified You Only Look Once v3 (YOLOv3) architecture is employed for mass detection by specifically assigning an extra number of higher-valued anchor boxes to the shallower detection head using the enhanced image. The experimental results show that the use of SbBDEM prior to training mass detection promotes superior performance with an increase in mean Average Precision (mAP) of 17.24% improvement over the non-enhanced trained image for mass detection, mass segmentation of 94.41% accuracy, and 96% accuracy for benign and malignant mass classification. Enhancing the mammogram images based on breast density is proven to increase the overall system's performance and can aid in an improved clinical diagnosis process.

Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms

Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided detection helps radiologists in detecting abnormalities efficiently, researchers across the world are striving to develop reliable models to deal with. One of the common approaches to identifying breast cancer is through breast mammograms. However, the identification of malignant breasts from mass lesions is a challenging research problem. In the current work, we propose a method for the classification of breast mass using mammograms which consists of two main stages. At first, we extract deep features from the input mammograms using the well-known VGG16 model while incorporating an attention mechanism into this model. Next, we apply a meta-heuristic called Social Ski-Driver (SSD) algorithm embedded with Adaptive Beta Hill Climbing based local search to obtain an optimal features subset. The optimal features subset is fed to the K-nearest neighbors (KNN) classifier for the classification. The proposed model is demonstrated to be very useful for identifying and differentiating malignant and healthy breasts successfully. For experimentation, we evaluate our model on the digital database for screening mammography (DDSM) database and achieve 96.07% accuracy using only 25% of features extracted by the attention-aided VGG16 model. The Python code of our research work is publicly available at: https://github.com/Ppayel/BreastLocalSearchSSD.

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

Automatic breast mass detection in mammograms using density of wavelet coefficients and a patch-based CNN.

For the purpose of accurate and efficient mass detection in full-field digital mammograms, weproposeamethodfor automated mass detection that consists oftwostages: suspicious region localization and false-positive (FP) reduction, by classifying these regions into mass and non-mass regions (normal tissues). In the first stage, the density of the wavelet coefficients based on Quincunx Lifting Scheme (DWC-QLS) is used to find suspicious regions (regions of interest, ROIs) in full mammograms. In the second stage, a patch-based CNN classifier is developed as an FP reduction to classify the suspicious regions. The main aim of this stage is to reduce the false-positive suspicious regions while keeping the true-positive suspicious regions. To further improve the performance of the FP reduction, the effectiveness of different transfer learning strategies is further explored and the best fine-tuning strategy in training InceptionV3 model is determined experimentally. The experimental results show that the proposed method can achieve an overall performance of 0.98 TPR @1.43 FPI on the INbreast database. In addition, the suggested segmentation method detects the mass location with 100% sensitivity and average of 5.4 false positives per image. Based on the obtained results,the introduced method was able to successfully detectand classify suspicious regions in digital mammograms and provide better TPR and FPI results in comparison with other state-of-the-art method.

Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy

Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with 1024×1024 pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles method, Fuzzy c-means (FCM) clustering and SVM combination method, and FCM clustering based SVM model) and comparison with SVM have been performed in this work. The segmentation by FCM clustering technique allows one piece of data to belong in two or more clusters. The additional parts are due to the segmented image to enhance the tumor-shape. Simulation provides the accuracy and the area under the ROC curve for mini-MIAS are 91.39% and 0.964 respectively which give the confirmation of the effectiveness of the proposed algorithm (FCM-based SVM). This method increases the classification accuracy in the case of a malignant tumor. The simulation is based on R-software.

Microcalcification Detection in Mammograms Using Particle Swarm Optimization and Probabilistic Neural Network

Breast cancer is the most typical form of cancer among the female population and the most common form of cancer-related death. However, if the cancer is detected at an early stage, treatment may be more effective. Mammography is one of the most used imaging modalities for the early breast cancer diagnosis. The present paper proposes an intelligent system for the detection and analysis of microcalcifications in mammography using the region growing algorithm, the particle swarm optimization algorithm (PSO), and the Probabilistic neural network (PNN) to detect the presence of breast cancer as early as possible and to avoid resorting to ablation of the breast.

Comparative Study and Analysis of Recent Computer Aided Diagnosis Systems for Masses Detection in Mammograms

Nowadays, breast cancer is considered one of the most threatening and common cancers for women due to the high rate of deaths that occurred yearly that reaches about 25% in all cancers. One of the most keys to decrease the mortal rate caused by breast cancer is its early detection. So, the research on developing computer-aided diagnosis systems (CADs) has been widely increased to improve the accuracy of breast cancer localization and classification. Generally, a proposed CAD is developed through four stages: data preparation and preprocessing, cancer detection, followed by its pathology classification. In this paper, the most recent proposed approaches to detect lesions in the breast mammograms and classify them are discussed with a comparative analysis to list the advantages and the disadvantages of most approaches. The main objective of this paper is to group the CADs with a performance evaluation and detailed analysis in order to furtherly develop others by avoiding the main weak points in the existing systems and to achieve high detection accuracy and classification performance at the same time.

Breast Cancer Mass Detection in Mammograms Using Gray Difference Weight and MSER Detector

Breast cancer is a deadly and one of the most prevalent cancers in women across the globe. Mammography is widely used imaging modality for diagnosis and screening of breast cancer. Segmentation of breast region and mass detection are crucial steps in automatic breast cancer detection. Due to the non-uniform distribution of various tissues, it is a challenging task to analyze mammographic images with high accuracy. In this paper, background suppression and pectoral muscle removal are performed using gradient weight map followed by gray difference weight and fast marching method. Enhancement of breast region is performed using contrast limited adaptive histogram equalization (CLAHE) and de-correlation stretch. Detection of breast masses is accomplished by gray difference weight and maximally stable external regions (MSER) detector. Experimentation on Mammographic Image Analysis Society (MIAS) and curated breast imaging subset of digital database for screening mammography (CBIS-DDSM) show that the method proposed performs breast boundary segmentation and mass detection with best accuracies. Mass detection achieved high accuracies of about 97.64% and 94.66% for MIAS and CBIS-DDSM dataset, respectively. The method is simple, robust, less affected to noise, density, shape and size which could provide reasonable support for mammographic analysis.

A Novel Hybrid Image Segmentation Method for Detection of Suspicious Regions in Mammograms Based on Adaptive Multi-Thresholding (HCOW)

Suspicious region segmentation is one of the most important parts of CAD systems that are used for breast cancer detection in mammograms. In a CAD system, there can be so many suspicious regions determined for a mammogram because of the complex structure of the breast. This study proposes a hybrid thresholding method to use in the CAD systems for efficient segmentation of the mammograms and reducing the number of the suspicious regions. The proposed method provides fully-automatic segmentation of the suspicious regions. This method is based on determining an adaptive multi-threshold value by using three different techniques together. These techniques are Otsu multilevel thresholding, Havrda & Charvat entropy, and w-BSAFCM algorithm that was developed by the authors of this paper for image clustering applications. In the proposed method, segmentation of a mammogram is performed on two sub-images obtained from that mammogram, the pectoral muscle and the breast region to prevent any information loss. The method was tested on 55 mass-mammograms and 210 non-mass mammograms of the mini-MIAS database, and it was compared with Shannon, Renyi, and Kapur entropy methods and with some of the related studies from the literature. The segmentation results of the tests were evaluated in terms of the number of suspicious regions, the number of correctly detected masses, and the performance measure parameters, accuracy, false-positive rate, specificity, volumetric overlap, and dice similarity coefficient. According to the evaluations, it was shown that the proposed method can both successfully locate the mass regions and significantly reduce the number of the non-mass suspicious regions on the mammograms.