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New AI Detects Breast Cancer from Ultrasounds

#artificialintelligence

Artificial intelligence (AI) machine learning is rapidly transforming how physicians, clinicians, pathologists, and health care providers diagnose patient conditions. A recent NYU Langone Health study published in Nature Communications shows how AI applied to ultrasound images can identify breast cancer with radiologist-level accuracy, reduce requested biopsies by 27.8 percent, and significantly decrease false positive rates of breast cancer by 37 percent. "In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images," wrote Krzysztof Geras, PhD., the study senior investigator and assistant professor at NYU Grossman School of Medicine, in collaboration with co-investigator and radiologist Linda Moy, MD. a professor at NYU Grossman School of Medicine, and their research colleagues. Both Geras and Moy are members of the Perlmutter Cancer Center. Breast cancer is a leading cause of death among women worldwide.


The Untapped Social Impact of Artificial Intelligence for Breast Cancer Screening in Developing Countries: A Critical Commentary of DeepMind

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Although the potential of such technology to reduce the global disease burden of breast cancer is significant, there are a number of pitfalls in DeepMind's research that will hinder it from being universally adopted as a true replacement for a radiologist in the developing world. First is that the DeepMind team failed to disclose the technical architecture of their AI system. The second criticism is that DeepMind chose to exclusively use datasets from predominantly Caucasian patients. Although DeepMind made a strong case for the use of supplementary local data, clearly any attempt to create models that generalize globally require supplementary data from a number of diverse ethnic regions. Moreover, any models that are designed to generalize globally and be used in the developing world would require testing and validation in a variety of ethnogeographic regions.


Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning

arXiv.org Artificial Intelligence

Complication risk profiling is a key challenge in the healthcare domain due to the complex interaction between heterogeneous entities (e.g., visit, disease, medication) in clinical data. With the availability of real-world clinical data such as electronic health records and insurance claims, many deep learning methods are proposed for complication risk profiling. However, these existing methods face two open challenges. First, data heterogeneity relates to those methods leveraging clinical data from a single view only while the data can be considered from multiple views (e.g., sequence of clinical visits, set of clinical features). Second, generalized prediction relates to most of those methods focusing on single-task learning, whereas each complication onset is predicted independently, leading to suboptimal models. We propose a multi-view multi-task network (MuViTaNet) for predicting the onset of multiple complications to tackle these issues. In particular, MuViTaNet complements patient representation by using a multi-view encoder to effectively extract information by considering clinical data as both sequences of clinical visits and sets of clinical features. In addition, it leverages additional information from both related labeled and unlabeled datasets to generate more generalized representations by using a new multi-task learning scheme for making more accurate predictions. The experimental results show that MuViTaNet outperforms existing methods for profiling the development of cardiac complications in breast cancer survivors. Furthermore, thanks to its multi-view multi-task architecture, MuViTaNet also provides an effective mechanism for interpreting its predictions in multiple perspectives, thereby helping clinicians discover the underlying mechanism triggering the onset and for making better clinical treatments in real-world scenarios.


Identifying Women with Mammographically-Occult Breast Cancer Leveraging GAN-Simulated Mammograms

arXiv.org Artificial Intelligence

Our objective is to show the feasibility of using simulated mammograms to detect mammographically-occult (MO) cancer in women with dense breasts and a normal screening mammogram who could be triaged for additional screening with magnetic resonance imaging (MRI) or ultrasound. We developed a Conditional Generative Adversarial Network (CGAN) to simulate a mammogram with normal appearance using the opposite mammogram as the condition. We used a Convolutional Neural Network (CNN) trained on Radon Cumulative Distribution Transform (RCDT) processed mammograms to detect MO cancer. For training CGAN, we used screening mammograms of 1366 women. For MO cancer detection, we used screening mammograms of 333 women (97 MO cancer) with dense breasts. We simulated the right mammogram for normal controls and the cancer side for MO cancer cases. We created two RCDT images, one from a real mammogram pair and another from a real-simulated mammogram pair. We finetuned a VGG16 on resulting RCDT images to classify the women with MO cancer. We compared the classification performance of the CNN trained on fused RCDT images, CNN_{Fused} to that of trained only on real RCDT images, CNN_{Real}, and to that of trained only on simulated RCDT images, CNN_{Simulated}. The test AUC for CNN_{Fused} was 0.77 with a 95% confidence interval (95CI) of [0.71, 0.83], which was statistically better (p-value < 0.02) than the CNN_{Real} AUC of 0.70 with a 95CI of [0.64, 0.77] and CNN_{Simulated} AUC of 0.68 with a 95CI of [0.62, 0.75]. It showed that CGAN simulated mammograms can help MO cancer detection.


Mass Segmentation in Automated 3-D Breast Ultrasound Using Dual-Path U-net

arXiv.org Artificial Intelligence

Automated 3-D breast ultrasound (ABUS) is a newfound system for breast screening that has been proposed as a supplementary modality to mammography for breast cancer detection. While ABUS has better performance in dense breasts, reading ABUS images is exhausting and time-consuming. So, a computer-aided detection system is necessary for interpretation of these images. Mass segmentation plays a vital role in the computer-aided detection systems and it affects the overall performance. Mass segmentation is a challenging task because of the large variety in size, shape, and texture of masses. Moreover, an imbalanced dataset makes segmentation harder. A novel mass segmentation approach based on deep learning is introduced in this paper. The deep network that is used in this study for image segmentation is inspired by U-net, which has been used broadly for dense segmentation in recent years. The system's performance was determined using a dataset of 50 masses including 38 malign and 12 benign lesions. The proposed segmentation method attained a mean Dice of 0.82 which outperformed a two-stage supervised edge-based method with a mean Dice of 0.74 and an adaptive region growing method with a mean Dice of 0.65.


La veille de la cybersécurité

#artificialintelligence

September 09, 2021 – Deep learning can distinguish between the mammograms of women who will later develop breast cancer and those who will not, according to new research out of the University of Hawaii. Researchers said the findings show the potential of artificial intelligence to act as a second reader for radiologists, reducing unnecessary imaging and associated costs. Annual mammography is recommended for women to screen for breast cancer starting at the age of 40. Research indicates that screening mammography lowers breast cancer mortality by decreasing the likelihood of cancer advancing undetected. Mammograms not only assist in detecting cancer but can also predict breast cancer risk by measuring breast density.


Researchers use deep learning to predict breast cancer risk

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Compared with commonly used clinical risk factors, a sophisticated type of artificial intelligence (AI) called deep learning does a better job distinguishing between the mammograms of women who will later develop breast cancer and those who will not, according to a new study in the journal Radiology. Researchers said the findings underscore AI's potential as a second reader for radiologists that can reduce unnecessary imaging and associated costs. Annual mammography is recommended for women starting at age 40 to screen for breast cancer. Research has shown that screening mammography lowers breast cancer mortality by reducing the incidence of advanced cancer. Mammograms not only help detect cancer but also provide a measure of breast cancer risk through measurements of breast density.


Enhancing Unsupervised Anomaly Detection with Score-Guided Network

arXiv.org Artificial Intelligence

Anomaly detection plays a crucial role in various real-world applications, including healthcare and finance systems. Owing to the limited number of anomaly labels in these complex systems, unsupervised anomaly detection methods have attracted great attention in recent years. Two major challenges faced by the existing unsupervised methods are: (i) distinguishing between normal and abnormal data in the transition field, where normal and abnormal data are highly mixed together; (ii) defining an effective metric to maximize the gap between normal and abnormal data in a hypothesis space, which is built by a representation learner. To that end, this work proposes a novel scoring network with a score-guided regularization to learn and enlarge the anomaly score disparities between normal and abnormal data. With such score-guided strategy, the representation learner can gradually learn more informative representation during the model training stage, especially for the samples in the transition field. We next propose a score-guided autoencoder (SG-AE), incorporating the scoring network into an autoencoder framework for anomaly detection, as well as other three state-of-the-art models, to further demonstrate the effectiveness and transferability of the design. Extensive experiments on both synthetic and real-world datasets demonstrate the state-of-the-art performance of these score-guided models (SGMs).


Deep Learning Artificial Intelligence Predicts Breast Cancer Risk Better

#artificialintelligence

Compared with commonly used clinical risk factors, a sophisticated type of artificial intelligence (AI) called deep learning does a better job distinguishing between the mammograms of women who will later develop breast cancer and those who will not, according to a new study in the journal Radiology. Researchers said the findings underscore AI's potential as a second reader for radiologists that can reduce unnecessary imaging and associated costs. Annual mammography is recommended for women starting at age 40 to screen for breast cancer. Research has shown that screening mammography lowers breast cancer mortality by reducing the incidence of advanced cancer. Mammograms not only help detect cancer but also provide a measure of breast cancer risk through measurements of breast density.


Study: Deep learning artificial intelligence predicts breast cancer risk better

#artificialintelligence

According to a new study in the journal Radiology, AI-driven deep learning may be better at distinguishing between the mammograms of women who will later develop breast cancer and those who will not.