Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach

The mammography image eccentric area is the breast density percentage measurement. The technical challenge of quantification in radiology leads to misinterpretation in screening. Data feedback from society, institutional, and industry shows that quantification and segmentation frameworks have rapidly become the primary methodologies for structuring and interpreting mammogram digital images. Segmentation clustering algorithms have setbacks on overlapping clusters, proportion, and multidimensional scaling to map and leverage the data. In combination, mammogram quantification creates a long-standing focus area. The algorithm proposed must reduce complexity and target data points distributed in iterative, and boost cluster centroid merged into a single updating process to evade the large storage requirement. The mammogram database's initial test segment is critical for evaluating performance and determining the Area Under the Curve (AUC) to alias with medical policy. In addition, a new image clustering algorithm anticipates the need for largescale serial and parallel processing. There is no solution on the market, and it is necessary to implement communication protocols between devices. Exploiting and targeting utilization hardware tasks will further extend the prospect of improvement in the cluster. Benchmarking their resources and performance is required. Finally, the medical imperatives cluster was objectively validated using qualitative and quantitative inspection. The proposed method should overcome the technical challenges that radiologists face

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

Studies on deep learning approach in breast lesions detection and cancer diagnosis in mammograms

Breast cancer accounts for the largest proportion of newly diagnosed cancers in women recently. Early diagnosis of breast cancer can improve treatment outcomes and reduce mortality. Mammography is convenient and reliable, which is the most commonly used method for breast cancer screening. However, manual examinations are limited by the cost and experience of radiologists, which introduce a high false positive rate and false examination. Therefore, a high-performance computer-aided diagnosis (CAD) system is significant for lesions detection and cancer diagnosis. Traditional CADs for cancer diagnosis require a large number of features selected manually and remain a high false positive rate. The methods based on deep learning can automatically extract image features through the network, but their performance is limited by the problems of multicenter data biases, the complexity of lesion features, and the high cost of annotations. Therefore, it is necessary to propose a CAD system to improve the ability of lesion detection and cancer diagnosis, which is optimized for the above problems. This thesis aims to utilize deep learning methods to improve the CADs' performance and effectiveness of lesion detection and cancer diagnosis. Starting from the detection of multi-type lesions using deep learning methods based on full consideration of characteristics of mammography, this thesis explores the detection method of microcalcification based on multiscale feature fusion and the detection method of mass based on multi-view enhancing. Then, a classification method based on multi-instance learning is developed, which integrates the detection results from the above methods, to realize the precise lesions detection and cancer diagnosis in mammography. For the detection of microcalcification, a microcalcification detection network named MCDNet is proposed to overcome the problems of multicenter data biases, the low resolution of network inputs, and scale differences between microcalcifications. In MCDNet, Adaptive Image Adjustment mitigates the impact of multicenter biases and maximizes the input effective pixels. Then, the proposed pyramid network with shortcut connections ensures that the feature maps for detection contain more precise localization and classification information about multiscale objects. In the structure, trainable Weighted Feature Fusion is proposed to improve the detection performance of both scale objects by learning the contribution of feature maps in different stages. The experiments show that MCDNet outperforms other methods on robustness and precision. In case the average number of false positives per image is 1, the recall rates of benign and malignant microcalcification are 96.8% and 98.9%, respectively. MCDNet can effectively help radiologists detect microcalcifications in clinical applications. For the detection of breast masses, a weakly supervised multi-view enhancing mass detection network named MVMDNet is proposed to solve the lack of lesion-level labels. MVMDNet can be trained on the image-level labeled dataset and extract the extra localization information by exploring the geometric relation between multi-view mammograms. In Multi-view Enhancing, Spatial Correlation Attention is proposed to extract correspondent location information between different views while Sigmoid Weighted Fusion module fuse diagnostic and auxiliary features to improve the precision of localization. CAM-based Detection module is proposed to provide detections for mass through the classification labels. The results of experiments on both in-house dataset and public dataset, [email protected] and [email protected] (recall rate@average number of false positive per image), demonstrate MVMDNet achieves state-of-art performances among weakly supervised methods and has robust generalization ability to alleviate the multicenter biases. In the study of cancer diagnosis, a breast cancer classification network named CancerDNet based on Multi-instance Learning is proposed. CancerDNet successfully solves the problem that the features of lesions are complex in whole image classification utilizing the lesion detection results from the previous chapters. Whole Case Bag Learning is proposed to combined the features extracted from four-view, which works like a radiologist to realize the classification of each case. Low-capacity Instance Learning and High-capacity Instance Learning successfully integrate the detections of multi-type lesions into the CancerDNet, so that the model can fully consider lesions with complex features in the classification task. CancerDNet achieves the AUC of 0.907 and AUC of 0.925 on the in-house and the public datasets, respectively, which is better than current methods. The results show that CancerDNet achieves a high-performance cancer diagnosis. In the works of the above three parts, this thesis fully considers the characteristics of mammograms and proposes methods based on deep learning for lesions detection and cancer diagnosis. The results of experiments on in-house and public datasets show that the methods proposed in this thesis achieve the state-of-the-art in the microcalcifications detection, masses detection, and the case-level classification of cancer and have a strong ability of multicenter generalization. The results also prove that the methods proposed in this thesis can effectively assist radiologists in making the diagnosis while saving labor costs

Prototipo de un sistema de diagnóstico asistido por ordenador orientado a la localización de clusters de microcalcificaciones en mamografías

En este trabajo se presenta la construcción metodológica para la implementación de un prototipo de aplicativo software que sirva como herramienta de apoyo al diagnóstico de cáncer de mama, a partir de las diferentes técnicas de procesamiento de imágenes y modelos de aprendizaje supervisado y no-supervisado. Tiene como aporte fundamental el hecho de que es una metodología que acopla diferentes etapas de procesamiento bastante robustas que permiten hacer un tratamiento desde la imagen mamográfica en crudo hasta la recomendación final dada por el sistema (End-to-End). En particular se consideró la técnica de realce de contraste de corrección gamma adaptativa con ponderación distribuida (AGCWD) y binarización de Otsu para la segmentación del tejido mamario, el segmentador K-means para la identificación del musculo pectoral, una red neuronal convolucional (CNN) para la localización de microcalcificaciones, un ensamble de redes neuronales artificiales (RNA) responsable de la clasificación y del proceso de búsqueda de imágenes similares. Además, se usó la librería tkinter para la implementación de la interfaz gráfica de usuario (GUI) en Python. Para la validación de la metodología se usaron dos bases de datos, The Mammographic Image Analysis (mini-MIAS) y The Digital Database for Screening Mammography (DDSM). Image Analysis (mini-MIAS) y The Digital Database for Screening Mammography (DDSM). Los resultados obtenidos reflejan que esta metodología mejora sustancialmente el rendimiento en la eliminación de artefactos (99.78%), la precisión en la remoción del musculo pectoral (92.14%), la reducción de falsos positivos en la detección de microcalcificaciones (0.47 por imagen), y aumento en el acierto en la clasificación según el estándar BI-RADS (82%) en comparación a otros trabajos en el estado del arte.This work presents the methodological construction for the implementation of a prototype software application that serves as a support tool for breast cancer diagnosis, based on different image processing techniques and supervised and unsupervised learning models. Its fundamental contribution is the fact that it is a methodology that couples different processing stages quite robust that allow a treatment from the raw mammographic image to the final recommendation given by the system (End-to-End). In particular, the contrast enhancement with adaptive gamma correction weighting distribution (AGCWD) and Otsu binarization technique was considered for the segmentation of breast tissue, the K-means segmenter for the identification of pectoral muscle, a convolutional neural network (CNN) for the localization of microcalcifications, an assembly of artificial neural networks (ANN) responsible for the classification and the search process of similar images. In addition, the tkinter library was used for the implementation of the graphical user interface (GUI) in Python. Two databases, The Mammographic Image Analysis (mini-MIAS) and The Digital Database for Screening Mammography (DDSM), were used to validate the methodology. The results obtained reflect that this methodology substantially improves the performance in the elimination of artifacts (99.78%), the accuracy in the removal of the pectoral muscle (92.14%), the reduction of false positives in the detection of microcalcifications (0.47 per image), and the increase in the accuracy in the classification according to the BI RADS standard (82%) in comparison to other works in the state of the artMaestríaMagíster en Ingeniería EléctricaIndice 1. Resumen 1 2. Abstract 2 3. Introducción 3 4. Planteamiento del problema 4 5. Justificación 8 6. Objetivos 9 6.1. General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 6.2. Específicos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 7. Revision del estado del arte 10 7.1. Eliminacion de artefactos y remoción del musculo pectoral . . . . . . 10 7.2. Deteccion de clusters de microcalcificaciones . . . . . . . . . . . . . . 11 7.3. Clasificación de clusters de microcalcificaciones . . . . . . . . . . . . . 12 8. Marco Teórico 14 8.1. Eliminacion de ruido y supresion de artefactos . . . . . . . . . . . . . 15 8.1.1. Realce de contraste por corrección gamma adaptativa con ponderación distribuida . . . . . . 15 8.1.2. Segmentación del tejido mamario . . . . . . . . . . . . . . . . 16 8.2. Remocion del musculo pectoral . . . . . . . . . . . . . . . . . . . . . 18 8.2.1. Segmentacion del musculo pectoral con K-medias . . . . . . . 19 8.2.2. Correccion del contorno con aproximación polinomial . . . . . 20 8.3. Deteccion y localizaci´on de microcalcificaciones . . . . . . . . . . . . 22 8.3.1. Deteccion de MC con CNN . . . . . . . . . . . . . . . . . . . 22 8.3.2. Realce de contraste, segmentación y filtrado de MC . . . . . . 24 8.4. Clasificación de microcalcificaciones según su categoría BI-RADS . . 26 8.4.1. Escala BI-RADS . . . . . . . . . . . . . . . . . . . . . . . . . 26 8.4.2. Extracci´on de características . . . . . . . . . . . . . . . . . . . 27 8.4.3. Redes Neuronales Artificiales . . . . . . . . . . . . . . . . . . 27 8.4.4. Sistema de recuperación de imágenes de microcalcificaciones . 28 9. Marco experimental 30 9.1. Bases de datos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 9.2. Resultados de las pruebas de eliminación de artefactos . . . . . . . . 31 9.3. Resultados de las pruebas de remoción del musculo pectoral . . . . . 32 9.4. Resultados de las pruebas de detección y localización de MC . . . . . 34 9.5. Resultados de las pruebas de clasificacion de MC según su categoría BI-RADS . . . . . . 36 9.6. Diseño de la interfaz del sistema DAO . . . . . . . . . . . . . . . . . 37 10.Conclusiones 41 11.Resultados académicos 44 12.Agradecimientos 45 13.Bibliografía 4