scholarly journals A novel deep learning architecture outperforming ‘off‑the‑shelf’ transfer learning and feature‑based methods in the automated assessment of mammographic breast density

2019 ◽  
Author(s):  
Eleftherios Trivizakis ◽  
Georgios Ioannidis ◽  
Vasileios Melissianos ◽  
Georgios Papadakis ◽  
Aristidis Tsatsakis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Automated Assessment ◽  
Feature Based

scholarly journals Deep Learning for Mammographic Breast Density Assessment and Beyond

Radiology ◽  
2019 ◽  
Vol 290 (1) ◽  
pp. 59-60 ◽  
Author(s):  
Heang-Ping Chan ◽  
Mark A. Helvie
Keyword(s):  
Deep Learning ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Density Assessment

Impact of a deep learning model to reduce variation and costs of federally mandated breast density legislation.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1550-1550
Author(s):  
Katherine Cavallo Hom ◽  
Brian Nicholas Dontchos ◽  
Sarah Mercaldo ◽  
Pragya Dang ◽  
Leslie Lamb ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Practice ◽  
Breast Density ◽  
Routine Clinical Practice ◽  
Dense Breast ◽  
Wilcoxon Test ◽  
Screening Tests ◽  
Mammographic Breast Density ◽  
Community Practice ◽  
Screening Mammograms

1550 Background: Dense breast tissue is an independent risk factor for malignancy and can mask cancers on mammography. Yet, radiologist-assessed mammographic breast density is subjective and varies widely between and within radiologists. Our deep learning (DL) model was implemented into routine clinical practice at an academic breast imaging center and was externally validated at a separate community practice, with both sites demonstrating high clinical acceptance of the model’s density predictions. The aim of this study is to demonstrate the influence our DL model has on prospective radiologist density assessments in routine clinical practice. Methods: This IRB-approved, HIPAA-compliant retrospective study identified consecutive screening mammograms without exclusion performed across three clinical sites, over two time periods: pre-DL model implementation (January 1, 2017 through September 30, 2017) and post-DL model implementation (January 1, 2019 through September 30, 2019). Clinical sites were as follows: Site A (the academic practice where the DL model was developed and was implemented in late 2017); Site B (an affiliated community practice which implemented the DL model in late 2017 and was used for external validation); and Site C (an affiliated community practice which was never exposed to the DL model). Patient demographics and radiologist-assessed mammographic breast densities were compared over time and across sites. Patient characteristics were evaluated using Wilcoxon test and Pearson’s chi-squared test. Multivariable logistic regression models evaluated the odds of a dense breast classification as a function of time period (pre-DL vs post-DL), race (White vs non-White) and site. Results: A total of 85,865 consecutive screening mammograms across the three clinical sites were identified. After controlling for age and race, adjusted odds ratios (aOR) of a mammogram being classified as dense at Site C compared to Site B before the DL model was implemented was 2.01 (95% CI 1.873, 2.157, p<0.001). This increased to 2.827 (95% CI 2.636, 3.032, p< 0.001) after DL implementation. The aOR of a mammogram being classified as dense at Site A after implementation compared to before implementation was 0.924 (95% CI 0.885, 0.964, p<0.001). Conclusions: Our findings suggest implementation of the DL model influences radiologist’s prospective density assessments in routine clinical practice by reducing the odds of a screening exam being categorized as dense. As a result, clinical use of our model could reduce downstream costs of supplemental screening tests and limit unnecessary high-risk clinic evaluations.[Table: see text]

scholarly journals Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective

2017 ◽  
Vol 31 (4) ◽  
pp. 387-392 ◽  
Author(s):  
Aly A. Mohamed ◽  
Yahong Luo ◽  
Hong Peng ◽  
Rachel C. Jankowitz ◽  
Shandong Wu
Keyword(s):  
Deep Learning ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Density Assessment

scholarly journals Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning

2018 ◽  
Vol 63 (2) ◽  
pp. 025005 ◽  
Author(s):  
Songfeng Li ◽  
Jun Wei ◽  
Heang-Ping Chan ◽  
Mark A Helvie ◽  
Marilyn A Roubidoux ◽  
...  
Keyword(s):  
Deep Learning ◽  
Statistical Learning ◽  
Breast Density ◽  
Computer Aided ◽  
Feature Based

scholarly journals Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation

Radiology ◽  
2019 ◽  
Vol 290 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Constance D. Lehman ◽  
Adam Yala ◽  
Tal Schuster ◽  
Brian Dontchos ◽  
Manisha Bahl ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Clinical Implementation ◽  
Density Assessment

scholarly journals A deep learning method for classifying mammographic breast density categories

2017 ◽  
Vol 45 (1) ◽  
pp. 314-321 ◽  
Author(s):  
Aly A. Mohamed ◽  
Wendie A. Berg ◽  
Hong Peng ◽  
Yahong Luo ◽  
Rachel C. Jankowitz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Learning Method

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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