Query: cancer tomosynthesis images with deep learning code

title: A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images
abstract: Question Can a curated, annotated, and publicly available data set of digital breast tomosynthesis (DBT) volumes be created for the development and validation of breast cancer computer aided detection algorithms?  In this diagnostic study, a curated and annotated data set of DBT studies that contained 22 032 reconstructed DBT volumes from 5060 patients was made publicly available. A deep learning algorithm for breast cancer detection was developed and tested, with a sensitivity of 65% on a test set. Meaning In this study, the publicly available data set, alongside the deep learning model, could significantly advance the research on machine learning tools in breast cancer screening and medical imaging in general.

title: Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient specific radiation dose estimation
abstract: The two dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient specific radiation dose impossible. Digital breast tomosynthesis (DBT) a pseudo 3D technique, is now commonly used in breast cancer screening and diagnostics. Still, the severely limited 3rd dimension information in DBT has not been used, until now, to estimate the true breast density or the patient specific dose. This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks. The algorithm, which we name DBToR, is based on unrolling a proximal dual optimization method. The proximal operators are replaced with convolutional neural networks and prior knowledge is included in the model. This extends previous work on a deep learning based reconstruction model by providing both the primal and the dual blocks with breast thickness information, which is available in DBT. Training and testing of the model were performed using virtual patient phantoms from two different sources. Reconstruction performance, and accuracy in estimation of breast density and radiation dose, were estimated, showing high accuracy (density 3% dose 20% without bias, significantly improving on the current state of the art. This work also lays the groundwork for developing a deep learning based reconstruction algorithm for the task of image interpretation by radiologists.

title: Scatter correction with deep learning approach for contrast enhanced digital breast tomosynthesis (CEDBT) in both cranio caudal (CC) view and mediolateral oblique (MLO) view
abstract: Dual energy contrast enhanced digital breast tomosynthesis (CEDBT) uses weighted subtraction of two energy spectra to highlight tumor angiogenesis with uptake of iodinated contrast agent. The high energy scan contains more severe scatter radiation than regular low energy DBT. The purpose of this study is to develop a convolutional neural network (CNN) based scatter correction method for dual energy CEDBT in both craniocaudal (CC) view and mediolateral oblique (MLO) view. Anthropomorphic digital breast phantoms with various glandularity and 3D shape were generated using the VICTRE software tool developed by the FDA. The pectoralis muscle layer was inserted into the phantoms for MLO view. Projection images with and without scatter radiation were simulated using Monte Carlo (MC) simulation code of VICTRE, meeting the prototype Siemens Mammomat Inspiration CEDBT system with 300 mm thick a Se detector, 25 projections within 46 degree angular range. Scatter radiation ground truth was generated from MC simulated projection images to train CNN. Two separate U net CNNs were trained to predict scatter radiation maps. Mean absolute percentage error (MAPE) was used as the loss function. The average MAPE of this method is less than 3 from the ground truth of MC simulation. The proposed scatter correction method was then applied to clinical cases, demonstrating the reduction of cupping artifact and the improvement in contrast object conspicuity.

title: Survey of deep learning in breast cancer image analysis
abstract: Computer aided image analysis for better understanding of images has been time honored approaches in the medical computing field. In the conventional machine learning approach, the domain experts in medical images are mandatory for image annotation that subsequently to be used for feature engineering. However, in deep learning, a big jump has been made to help the researchers do segmentation, feature extraction, classification, and detection from raw medical images obtained using digital breast tomosynthesis, digital mammography, magnetic resonance imaging, and ultrasound imaging modalities. As a result, deep learning (DL) has gained a state of the art in many application areas, for example, breast cancer image analysis. In this survey paper, we reviewed the most common breast cancer imaging modalities, public, most cited and recently updated breast cancer databases, histopathological based breast cancer image analysis, and DL application types in medical image analysis. We finally conclude by pointing out the research gaps to be addressed in the future.

title: A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images
abstract: Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose a fully automated pipeline for oral cancer detection on whole slide cytology images. The pipeline consists of fully convolutional regression based nucleus detection, followed by per cell focus selection, and CNN based classification. Our novel focus selection step provides fast per cell focus decisions at human level accuracy. We demonstrate that the pipeline provides efficient cancer classification of whole slide cytology images, improving over previous results both in terms of accuracy and feasibility. The complete source code is available at this https URL.

title: Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review
abstract: The relatively recent reintroduction of deep learning has been a revolutionary force in the interpretation of diagnostic imaging studies. However, the technology used to acquire those images is undergoing a revolution itself at the very same time. Digital breast tomosynthesis (DBT) is one such technology, which has transformed the field of breast imaging. DBT, a form of three dimensional mammography, is rapidly replacing the traditional two dimensional mammograms. These parallel developments in both the acquisition and interpretation of breast images present a unique case study in how modern AI systems can be designed to adapt to new imaging methods. They also present a unique opportunity for co development of both technologies that can better improve the validity of results and patient outcomes. In this review, we explore the ways in which deep learning can be best integrated into breast cancer screening workflows using DBT. We first explain the principles behind DBT itself and why it has become the gold standard in breast screening. We then survey the foundations of deep learning methods in diagnostic imaging, and review the current state of research into AI based DBT interpretation. Finally, we present some of the limitations of integrating AI into clinical practice and the opportunities these present in this burgeoning field.

title: Adaptation of a deep learning malignancy model from full field digital mammography to digital breast tomosynthesis
abstract: Mammography based screening has helped reduce the breast cancer mortality rate, but has also been associated with potential harms due to low specificity, leading to unnecessary exams or procedures, and low sensitivity. Digital breast tomosynthesis (DBT) improves on conventional mammography by increasing both sensitivity and specificity and is becoming common in clinical settings. However, deep learning (DL) models have been developed mainly on conventional 2D full field digital mammography (FFDM) or scanned film images. Due to a lack of large annotated DBT datasets, it is difficult to train a model on DBT from scratch. In this work, we present methods to generalize a model trained on FFDM images to DBT images. In particular, we use average histogram matching (HM) and DL fine tuning methods to generalize a FFDM model to the 2D maximum intensity projection (MIP) of DBT images. In the proposed approach, the differences between the FFDM and DBT domains are reduced via HM and then the base model, which was trained on abundant FFDM images, is fine tuned. When evaluating on image patches extracted around identified findings, we are able to achieve similar areas under the receiver operating characteristic curve (ROC AUC) of 0:9 for FFDM and 0:85 for MIP images, as compared to a ROC AUC of 0:75 when tested directly on MIP images.

title: Performance comparison of different loss functions for digital breast tomosynthesis classification using 3D deep learning model
abstract: Artificial intelligence (AI) algorithms, especially deep learning methods have proven to be successful in many medical imaging applications. Computerized breast cancer image analysis can improve diagnosis accuracy. Digital Breast Tomosynthesis (DBT) imaging is a new modality and more advantageous compared to classical digital mammography (DM) Therefore, development of new deep learning algorithms compatible with DBT modality are potent to improve DBT imaging reading time efficiency and increase accuracy for breast cancer diagnosis when used as additional tool for radiologists. In this work, we aimed to build a 3D deep learning model to distinguish malignancy and benign breasts using DBT images. We also investigated effects of different loss functions in our deep learning models. We implemented and evaluated our method on a large data set of 546 patients (205 malignancy and 341 benign) Our results showed that different loss functions lead to an influence on the models performance in our classification tasks, and specific loss function may be selected or customized to adjust a specific performance metric for concrete applications.

title: Abstract PD9 03: A combination of intra and peri lesional deep learning classifiers from multiple views enables accurate diagnosis of architectural distortion malignancy with digital breast tomosynthesis
abstract: 3D digital breast tomosynthesis (DBT) offers greater sensitivity than 2D mammography to the presence of architectural distortions (AD) a suspicious finding requiring biopsy. This higher sensitivity to AD is only beneficial if DBT can differentiate benign AD, such as radial scars (RS) from malignant AD (MAD) Automated analysis of clinical images using a neural network has potential to enhance the accuracy of breast cancer diagnosis, but such applications focus on screening and currently struggle with more challenging diagnoses (e.g. MAD vs. RS) where computer assistance would provide great clinical utility. In this work, we define a deep learning (DL) framework utilizing amalgamate predictions from a group of neural networks, specialized for multiple lesion regions and different acquisitional views to distinguish MAD from RS.  A retrospective analysis was conducted of 69 patients screened at a single institution, each with AD visible on one or more DBT views. Stereotactic core biopsy determined 42 AD to be MAD and the remaining 27 to be RS. An attending breast radiologist manually identified a circular region of interest (ROI) containing the AD on a single slice of the mediolateral (ML, n=72) and/or craniocaudal (CC, n=68) views. 56 patients were utilized for training and tuning the DL networks in cross validation, while 25 volumes from 13 patients (8 MAD, 5 RS) were held for independent testing. Volumetric samples of size 24x24x12 pixels were extracted randomly within each ROI for input to the networks. Two neural networks were trained to separately recognize patterns of MAD at the intra and peri lesional region. Pairs of networks were trained separately for analysis of the CC and the MLO view, resulting in 4 possible network variations. These networks were first assessed individually by cross validation in the training set, then predictions from each were combined for diagnosis in the independent testing set. Performance was assessed by area under the receiver operating characteristic curve (AUC) sensitivity, specificity, and accuracy.  All networks individually achieved strong performance in distinguishing RS from MAD when assessed via cross validation in the training set. On the independent test set, networks trained to recognize patterns of malignancy at the center of the distortion outperformed those focused on the lesion margin. The CC view was found to be most discriminating in neural network assessment. When predictions from these networks were combined for diagnosis on the independent validation set, performance increased significantly to an AUC of 0.8, with a sensitivity of 0.6 and a specificity of 1.0.  In this investigation, we demonstrated that specialized deep learning classifiers, created to address the diagnosis of MAD vs RS, can assist in diagnostic settings without generating false negatives. Our approach combines neural networks to make a diagnosis based on various spatial regions from multiple DBT views. Our approach demonstrated very high specificity which may reduce the number of benign RS biopsies and surgical excisions for AD detected by DBT. Future work will further validate these findings on a larger, multi institutional cohort and explore their influence on clinical decision making.  Tristan Maidment, Nathaniel Braman, Yijiang Chen, Farhad Mehrkhani, Uliyana Yankevich, Donna Plecha, Anant Madabhushi. A combination of intra and peri lesional deep learning classifiers from multiple views enables accurate diagnosis of architectural distortion malignancy with digital breast tomosynthesis   San Antonio, TX. Philadelphia (PA) AACR;

title: Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5, 060 patients and a deep learning model
abstract: Breast cancer screening is one of the most common radiological tasks with over 39 million exams performed each year. While breast cancer screening has been one of the most studied medical imaging applications of artificial intelligence, the development and evaluation of the algorithms are hindered due to the lack of well annotated large scale publicly available datasets. This is particularly an issue for digital breast tomosynthesis (DBT) which is a relatively new breast cancer screening modality. We have curated and made publicly available a large scale dataset of digital breast tomosynthesis images. It contains 22,032 reconstructed DBT volumes belonging to 5,610 studies from 5,060 patients. This included four groups: (1) 5,129 normal studies, (2) 280 studies where additional imaging was needed but no biopsy was performed, (3) 112 benign biopsied studies, and (4) 89 studies with cancer. Our dataset included masses and architectural distortions which were annotated by two experienced radiologists. Additionally, we developed a single phase deep learning detection model and tested it using our dataset to serve as a baseline for future research. Our model reached a sensitivity of 65% at 2 false positives per breast. Our large, diverse, and highly curated dataset will facilitate development and evaluation of AI algorithms for breast cancer screening through providing data for training as well as common set of cases for model validation. The performance of the model developed in our study shows that the task remains challenging and will serve as a baseline for future model development.