SAFE-Diff: Scale-Aware Attention and Feature-Dispersive Diffusion with Uncertainty Estimation for Contrast-Enhanced Breast MRI Synthesis
Department of Medical Imaging, Radboud University Medical Center（鲁文大学医学中心医学影像部）；Department of Radiology, Netherlands Cancer Institute（荷兰癌症研究所放射科）；Maastro Clinic（马斯垂克诊所）；Faculty of Applied Science, Macao Polytechnic University（澳门理工大学应用科学学院）；Department of Radiation Oncology, Netherlands Cancer Institute（荷兰癌症研究所放射肿瘤科）
Synthesizing high fidelity contrast enhanced MRI is clinically valuable for safer and more efficient breast cancer screening, yet remains challenging due to complex lesion textures and heterogeneous enhancement patterns.
Mekelle Institute of Technology - Mekelle University（梅克莱大学梅克莱理工学院）；FDRE Ministry of Innovation and Technology（埃塞俄比亚联邦民主共和国创新与技术部）；College of Health Science - Mekelle University（梅克莱大学健康科学学院）
Breast cancer is prevalent in Ethiopia that accounts 34% among women cancer patients. The diagnosis technique in Ethiopia is manual which was proven to be tedious, subjective, and challenging. Deep learning techniques are revolutionizing the field of medical image analysis and hence in this study, we proposed Convolutional Neural Networks (CNNs) for breast mass detection so as to minimize the overheads of manual analysis. CNN architecture is designed for the feature extraction stage and adapted both the Region Proposal Network (RPN) and Region of Interest (ROI) portion of the faster R-CNN for …
The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Breast cancer is one of the most common and dangerous cancers impacting women worldwide. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful DCNN model that combines the strength of the Inception…
Diffeomorphic Multi-Resolution Deep Learning Registration for Applications in Breast MRI
Auckland Bioengineering Institute（奥克兰生物工程研究所）；University of Auckland（奥克兰大学）；Faculty of Medical and Health Sciences（医学与健康科学学院）；MGH/HST Martinos Center for Biomedical Imaging（麻省总医院/哈佛健康科学技术生物医学成像马蒂诺斯中心）；Harvard Medical School（哈佛医学院）；MIT（麻省理工学院）
Traditional breast cancer image classification methods require manual extraction of features from medical images, which not only require professional medical knowledge, but also have problems such as time-consuming and labor-intensive and difficulty in extracting high-quality features. Therefore, the paper proposes a computer-based feature fusion Convolutional neural network breast cancer image classification and detection method. The paper pre-trains two convolutional neural networks with different structures, and then uses the convolutional neural network to automatically extract the charact…
Evaluating ADC-only deep learning pipelines for breast cancer detection and segmentation using standalone diffusion-weighted MRI
University of Oviedo（奥维耶多大学）；University of A Coruña（拉科鲁尼亚大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Purdue University（普渡大学）；Asturias Central University Hospital (HUCA)（阿斯图里亚斯中央大学医院）
University of Pennsylvania（宾夕法尼亚大学）；Mayo Clinic（梅奥诊所）
Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast percentage density (PD) from digital mammograms. Our method leverages deep learning (DL) using two convolutional neural network arch…
University of Toronto（多伦多大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）
High predictive performance and ease of use and interpretability are important requirements for the applicability of a computer-aided diagnosis (CAD) to human reading studies. We propose a CAD system specifically designed to be more comprehensible to the radiologist reviewing screening breast MRI studies. Multiparametric imaging features are combined to produce a CAD system for differentiating cancerous and non-cancerous lesions. The complete system uses a rule-extraction algorithm to present lesion classification results in an easy to understand graph visualization.
GuidedMorph: Two-Stage Deformable Registration for Breast MRI
Department of Electrical and Computer Engineering, Duke University（电子工程系，杜克大学）；Department of Biostatistics & Bioinformatics, Duke University（生物统计学与生物信息学系，杜克大学）；Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida（健康结果与生物医学信息学系，医学院，佛罗里达大学）
Mansoura University（曼苏拉大学）；Higher Institute of Engineering and Technology（高等工程技术学院）
Breast cancer is one of the most serious types of cancer that can occur in women. The automatic diagnosis of breast cancer by analyzing histological images (HIs) is important for patients and their prognosis. The classification of HIs provides clinicians with an accurate understanding of diseases and allows them to treat patients more efficiently. Deep learning (DL) approaches have been successfully employed in a variety of fields, particularly medical imaging, due to their capacity to extract features automatically. This study aims to classify different types of breast cancer using HIs. In th…
Jalpaiguri Government Engineering College（贾尔派古里政府工程学院）
Invasive ductal carcinoma (IDC), which is also sometimes known as the infiltrating ductal carcinoma, is the most regular form of breast cancer. It accounts for about 80% of all breast cancers. According to the American Cancer Society, more than 180,000 women in the United States are diagnosed with invasive breast cancer each year. The survival rate associated with this form of cancer is about 77% to 93% depending on the stage at which they are being diagnosed. The invasiveness and the frequency of the occurrence of these disease makes it one of the difficult cancers to be diagnosed. Our propos…
In this paper, we develop a complete pipeline for stain normalization, segmentation, and classification of nuclei in hematoxylin and eosin (H&E) stained breast cancer histopathology images. In the first step, we use a CNN-based stain transfer technique to normalize the staining characteristics of (H&E) images. We then train a neural network to segment images of nuclei from the H&E images. Finally, we train an Information Maximizing Generative Adversarial Network (InfoGAN) to learn visual representations of different types of nuclei and classify them in an entirely unsupervised manner. The resu…
Early prediction of pathological complete response (pCR) following neoadjuvant chemotherapy (NAC) for breast cancer plays a critical role in surgical planning and optimizing treatment strategies. Recently, machine and deep-learning based methods were suggested for early pCR prediction from multi-parametric MRI (mp-MRI) data including dynamic contrast-enhanced MRI and diffusion-weighted MRI (DWI) with moderate success. We introduce PD-DWI, a physiologically decomposed DWI machine-learning model to predict pCR from DWI and clinical data. Our model first decomposes the raw DWI data into the vario…
University of California, San Diego（加利福尼亚大学圣地亚哥分校）；Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）；Polish-Japanese Academy of Information Technology（波兰-日本信息技术学院）；The Maria Skłodowska-Curie Memorial Cancer Centre and Institute of Oncology（玛丽·斯克洛多夫斯卡-居里纪念癌症中心和肿瘤研究所）
Deep learning algorithms, especially convolutional neural networks, have become a methodology of choice in medical image analysis. However, recent studies in computer vision show that even a small modification of input image intensities may cause a deep learning model to classify the image differently. In medical imaging, the distribution of image intensities is related to applied image reconstruction algorithm. In this paper we investigate the impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning. Due to high dynamic range raw ultrasoni…
Iran University of Science and Technology（伊朗科技大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Netherlands Cancer Institute（荷兰癌症研究所）
Purpose: We propose a deep learning-based computer-aided detection (CADe) method to detect breast lesions in ultrafast DCE-MRI sequences. This method uses both the three-dimensional spatial information and temporal information obtained from the early-phase of the dynamic acquisition. Methods: The proposed CADe method, based on a modified 3D RetinaNet model, operates on ultrafast T1 weighted sequences, which are preprocessed for motion compensation, temporal normalization, and are cropped before passing into the model. The model is optimized to enable the detection of relatively small breast le…
The Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Sidney Kimmel Comprehensive Cancer Center（西德尼·基梅尔综合癌症中心）；The Johns Hopkins University（约翰霍普金斯大学）；University of Kentucky（肯塔基大学）；University of Wisconsin School of Medicine and Public Health（威斯康星大学医学与公共卫生学院）
A new paradigm is beginning to emerge in Radiology with the advent of increased computational capabilities and algorithms. This has led to the ability of real time learning by computer systems of different lesion types to help the radiologist in defining disease. For example, using a deep learning network, we developed and tested a multiparametric deep learning (MPDL) network for segmentation and classification using multiparametric magnetic resonance imaging (mpMRI) radiological images. The MPDL network was constructed from stacked sparse autoencoders with inputs from mpMRI. Evaluation of MPD…
Faculty of Informatics and Computer Science（信息与计算机科学学院）；The British University in Egypt（埃及英国大学）；Faculty of Science, Suez Canal University（苏伊士运河大学理学院）
Breast cancer is one of the leading fatal disease worldwide with high risk control if early discovered. Conventional method for breast screening is x-ray mammography, which is known to be challenging for early detection of cancer lesions. The dense breast structure produced due to the compression process during imaging lead to difficulties to recognize small size abnormalities. Also, inter- and intra-variations of breast tissues lead to significant difficulties to achieve high diagnosis accuracy using hand-crafted features. Deep learning is an emerging machine learning technology that requires…
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
Characterization of breast lesions is an essential prerequisite to detect breast cancer in an early stage. Automatic segmentation makes this categorization method robust by freeing it from subjectivity and human error. Both spectral and morphometric features are successfully used for differentiating between benign and malignant breast lesions. In this thesis, we used empirical mode decomposition method for semi-automatic segmentation. Sonographic features like ehcogenicity, heterogeneity, FNPA, margin definition, Hurst coefficient, compactness, roundness, aspect ratio, convexity, solidity, for…
École de Technologie Supérieure, Université du Québec（魁北克大学高等技术学院）；Université de Rouen Normandie（鲁昂诺曼底大学）
Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning. We find out that the deep learning networks pretrained on ImageNet have better performance than the popular handcrafted features used for breast cancer histology images. The best feature extractor achieves an average accuracy of 79.30%. To improve the classification performance, a random forest di…
Ultrasound imaging plays an important role in breast lesion differentiation. However, diagnostic accuracy depends on ultrasonographer experience. Various computer aided diagnosis systems has been developed to improve breast cancer detection and reduce the number of unnecessary biopsies. In this study, our aim was to improve breast lesion classification based on the BI-RADS (Breast Imaging - Reporting and Data System). This was accomplished by combining the BI-RADS with morphological features which assess lesion boundary. A dataset of 214 lesion images was used for analysis. 30 morphological fe…
University of Washington（华盛顿大学）；University of Vermont（佛蒙特大学）
We trained and applied an encoder-decoder model to semantically segment breast biopsy images into biologically meaningful tissue labels. Since conventional encoder-decoder networks cannot be applied directly on large biopsy images and the different sized structures in biopsies present novel challenges, we propose four modifications: (1) an input-aware encoding block to compensate for information loss, (2) a new dense connection pattern between encoder and decoder, (3) dense and sparse decoders to combine multi-level features, (4) a multi-resolution network that fuses the results of encoder-dec…
Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）；University of California, San Diego（加州大学圣迭戈分校）
Morphological features play an important role in breast mass classification in sonography. While benign breast masses tend to have a well-defined ellipsoidal contour, shape of malignant breast masses is commonly ill-defined and highly variable. Various handcrafted morphological features have been developed over the years to assess this phenomenon and help the radiologists differentiate benign and malignant masses. In this paper we propose an automatic approach to morphology analysis, we express shapes of breast masses as points on the Kendall's shape manifold. Next, we use the full Procrustes …
Anguinus Sculpturae: Compositional Synthesis of Peak-Enhancement Breast DCE-MRI Scans
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；HIDSS4Health – Helmholtz Information and Data Science School for Health（HIDSS4Health – 亥姆霍兹健康信息与数据科学学校）；Helmholtz Imaging（亥姆霍兹成像中心）
Objective: To develop an automatic image normalization algorithm for intensity correction of images from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired by different MRI scanners with various imaging parameters, using only image information. Methods: DCE-MR images of 460 subjects with breast cancer acquired by different scanners were used in this study. Each subject had one T1-weighted pre-contrast image and three T1-weighted post-contrast images available. Our normalization algorithm operated under the assumption that the same type of tissue in different patient…
Tencent YouTu X-Lab（腾讯优图X实验室）；Beijing Institute of Technology（北京理工大学）；Tsinghua University（清华大学）；Peking University Shenzhen Hospital（北京大学深圳医院）
Breast lesion detection in ultrasound video is critical for computer-aided diagnosis. However, detecting lesion in video is quite challenging due to the blurred lesion boundary, high similarity to soft tissue and lack of video annotations. In this paper, we propose a semi-supervised breast lesion detection method based on temporal coherence which can detect the lesion more accurately. We aggregate features extracted from the historical key frames with adaptive key-frame scheduling strategy. Our proposed method accomplishes the unlabeled videos detection task by leveraging the supervision infor…
Breast tumor segmentation provides accurate tumor boundary, and serves as a key step toward further cancer quantification. Although deep learning-based approaches have been proposed and achieved promising results, existing approaches have difficulty in detecting small breast tumors. The capacity to detecting small tumors is particularly important in finding early stage cancers using computer-aided diagnosis (CAD) systems. In this paper, we propose a novel deep learning architecture called Small Tumor-Aware Network (STAN), to improve the performance of segmenting tumors with different size. The…
South China University of Technology（华南理工大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）
Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classif…
Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is widely used to complement ultrasound examinations and x-ray mammography during the early detection and diagnosis of breast cancer. However, images generated by various MRI scanners (e.g. GE Healthcare vs Siemens) differ both in intensity and noise distribution, preventing algorithms trained on MRIs from one scanner to generalize to data from other scanners successfully. We propose a method for image normalization to solve this problem. MRI normalization is challenging because it requires both normalizing intensity values and map…
Technical University of Munich（慕尼黑工业大学）；Stanford University School of Medicine（斯坦福大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians with diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form B-mode images for diagnosis. However, the various types of breast tissue, such as glandular, fat, and lesions, differ in sound speed. These differences can degrade the image reconstruction process. Alternatively, sound speed can be a powerful tool for identifying disease. To this end, we propose a deep-learning approach for sound speed estimation from in-phase and quadrature ultrasound signals. First, we d…
Peking University（北京大学）；Beijing Institute of Big Data Research（北京大数据研究院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Breast cancer is one of the most common cancers in women worldwide, and early detection can significantly reduce the mortality rate of breast cancer. It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer. And thus, it is the key to design an accurate computer-aided detection (CAD) system to capture multi-scale contextual features in a cancerous tissue. In this work, we present a novel graph convolutional neural network for histopathological image classification of breast cancer. The new method, named multi-scale graph wavelet neural ne…
Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model
Yonsei College of Medicine（延世大学医学院）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Yongin Severance Hospital（龙仁Severance医院）；Yonsei University College of Medicine（延世大学医学院）
University of Idaho（爱达荷大学）；University of Utah School of Medicine（犹他大学医学院）
Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape and location are important for further tumor quantification and classification. However, segmenting small tumors in ultrasound images is challenging, due to the speckle noise, varying tumor shapes and sizes among patients, and the existence of tumor-like image regions. Recently, deep learning-based approaches have achieved great success for biomedical image analysis, but current state-of-the-art approaches achieve poor performance for segmenting …
University of Malaya（马来亚大学）；University of Tehran（德黑兰大学）
Reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low cost method for early diagnosis of breast cancer. The accuracy of the diagnosis is however highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The leverage in deep convolutional neural network approaches provided solutions in efficient analysis of breast ultrasound images. In this study, we proposed a new framework for classification of breast cancer lesions by use of an attention module in modified VGG16 architect…
Sustainable Deep Learning-Based Breast Lesion Segmentation: Impact of Breast Region Segmentation on Performance
Norwegian University of Science and Technology（挪威科技大学）；More og Romsdal Hospital Trust（默勒-鲁姆斯达尔医院信托基金）；Stavanger University Hospital（斯塔万格大学医院）；The University of Stavanger（斯塔万格大学）；Akershus University Hospital（阿克什胡斯大学医院）；University of Oslo（奥斯陆大学）
Rician Denoising Diffusion Probabilistic Models For Sodium Breast MRI Enhancement
University of Cambridge（剑桥大学）；Universitat de Barcelona（巴塞罗那大学）；Haukeland University Hospital（豪克兰大学医院）；University of York（约克大学）；Manchester Metropolitan University（曼彻斯特城市大学）
Comparing Conditional Diffusion Models for Synthesizing Contrast-Enhanced Breast MRI from Pre-Contrast Images
Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain（巴塞罗那大学数学与计算机系）；Parc Taulí Hospital Universitari, Spain（帕尔克·塔利大学医院）；Hospital Germans Trias i Pujol, Spain（加泰罗尼亚Trias i Pujol医院）；Siemens Healthineers, America, LAM（西门子医疗）；Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain（加泰罗尼亚高级研究与教育研究所）；Computer Vision Center, Bellaterra, Spain（计算机视觉中心）
