A multi-reconstruction study of breast density estimation using Deep Learning

• URL: http://arxiv.org/abs/2202.08238v2
• Date: Thu, 17 Feb 2022 14:59:38 GMT
• Title: A multi-reconstruction study of breast density estimation using Deep Learning
• Authors: Vikash Gupta, Mutlu Demirer, Robert W. Maxwell, Richard D. White, Barbaros Selnur Erdal
• Abstract summary: Breast density estimation is one of the key tasks performed during a screening exam. Deep-learning studies for breast density estimation use only a single modality for training a neural network. In this paper, we show that a neural network trained on all the modalities at once performs better than a neural network trained on any single modality.
• Score: 0.9449650062296825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast density estimation is one of the key tasks in recognizing individuals predisposed to breast cancer. It is often challenging because of low contrast and fluctuations in mammograms' fatty tissue background. Most of the time, the breast density is estimated manually where a radiologist assigns one of the four density categories decided by the Breast Imaging and Reporting Data Systems (BI-RADS). There have been efforts in the direction of automating a breast density classification pipeline. Breast density estimation is one of the key tasks performed during a screening exam. Dense breasts are more susceptible to breast cancer. The density estimation is challenging because of low contrast and fluctuations in mammograms' fatty tissue background. Traditional mammograms are being replaced by tomosynthesis and its other low radiation dose variants (for example Hologic' Intelligent 2D and C-View). Because of the low-dose requirement, increasingly more screening centers are favoring the Intelligent 2D view and C-View. Deep-learning studies for breast density estimation use only a single modality for training a neural network. However, doing so restricts the number of images in the dataset. In this paper, we show that a neural network trained on all the modalities at once performs better than a neural network trained on any single modality. We discuss these results using the area under the receiver operator characteristics curves.

Related papers

• A2DMN: Anatomy-Aware Dilated Multiscale Network for Breast Ultrasound Semantic Segmentation [44.99833362998488]
  We propose a novel breast anatomy-aware network for capturing fine image details and a new smoothness term that encodes breast anatomy. It incorporates context information across multiple spatial scales to generate more accurate semantic boundaries. The results demonstrate the proposed method significantly improves the segmentation of the muscle, mammary, and tumor classes.
  arXiv  Detail & Related papers  (2024-03-22T18:31:24Z)
• Multi-Head Feature Pyramid Networks for Breast Mass Detection [48.24995569980701]
  We propose the multi-head feature pyramid module (MHFPN) to solve the problem of unbalanced focus of target boxes during feature map fusion. Experimental studies show that, comparing to the SOTA detection baselines, our method improves by 6.58% (in AP@50) and 5.4% (in TPR@50) on the commonly used INbreast dataset.
  arXiv  Detail & Related papers  (2023-02-22T03:02:52Z)
• High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection [48.88813637974911]
  Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. High-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. This study aims to improve the mass detection performance in high-density breasts using synthetic high-density full-field digital mammograms.
  arXiv  Detail & Related papers  (2022-09-20T15:57:12Z)
• Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images [84.03487786163781]
  We develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection modules. Our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation.
  arXiv  Detail & Related papers  (2021-04-05T13:15:22Z)
• Deep-LIBRA: Artificial intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment [2.0369879867185143]
  Current federal legislation mandates reporting of breast density for all women undergoing breast screening. We introduce an artificial intelligence (AI) method to estimate breast percentage density (PD) from digital mammograms.
  arXiv  Detail & Related papers  (2020-11-13T15:21:17Z)
• On segmentation of pectoralis muscle in digital mammograms by means of deep learning [1.7114784273243784]
  The present paper introduces a two-step segmentation strategy based on a combined use of data-driven prediction and graph-based image processing. The proposed method employs a convolutional neural network (CNN) which is designed to predict the location of breast-pectoral boundary. The results of comparative analysis show considerable improvement over state-of-the-art, while offering the possibility of model-free and fully automatic processing.
  arXiv  Detail & Related papers  (2020-08-29T03:38:11Z)
• A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms [13.842620686759616]
  Mammograms are commonly employed in the large scale screening of breast cancer. We propose a two-stage Multiple Instance Learning framework for image-level detection of malignancy. A global image-level feature is computed as a weighted average of patch-level features learned using a CNN. Our method performed well on the task of localization of masses with an average Precision/Recall of 0.76/0.80 and acheived an average AUC of 0.91 on the imagelevel classification task.
  arXiv  Detail & Related papers  (2020-04-24T13:06:47Z)
• Understanding the robustness of deep neural network classifiers for breast cancer screening [52.50078591615855]
  Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented. We measure the sensitivity of a radiologist-level screening mammogram image classifier to four commonly studied input perturbations. We also perform a detailed analysis on the effects of low-pass filtering, and find that it degrades the visibility of clinically meaningful features.
  arXiv  Detail & Related papers  (2020-03-23T01:26:36Z)
• Two-stage multi-scale breast mass segmentation for full mammogram analysis without user intervention [2.7490008316742096]
  Mammography is the primary imaging modality used for early detection and diagnosis of breast cancer. Among diverse types of breast abnormalities, masses are the most important clinical findings of breast carcinomas. We present a two-stage multi-scale pipeline that provides accurate mass contours from high-resolution full mammograms.
  arXiv  Detail & Related papers  (2020-02-27T13:16:22Z)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.