3 research outputs found
Improving Breast Cancer Detection using Symmetry Information with Deep Learning
Convolutional Neural Networks (CNN) have had a huge success in many areas of
computer vision and medical image analysis. However, there is still an immense
potential for performance improvement in mammogram breast cancer detection
Computer-Aided Detection (CAD) systems by integrating all the information that
the radiologist utilizes, such as symmetry and temporal data. In this work, we
proposed a patch based multi-input CNN that learns symmetrical difference to
detect breast masses. The network was trained on a large-scale dataset of 28294
mammogram images. The performance was compared to a baseline architecture
without symmetry context using Area Under the ROC Curve (AUC) and Competition
Performance Metric (CPM). At candidate level, AUC value of 0.933 with 95%
confidence interval of [0.920, 0.954] was obtained when symmetry information is
incorporated in comparison with baseline architecture which yielded AUC value
of 0.929 with [0.919, 0.947] confidence interval. By incorporating symmetrical
information, although there was no a significant candidate level performance
again (p = 0.111), we have found a compelling result at exam level with CPM
value of 0.733 (p = 0.001). We believe that including temporal data, and adding
benign class to the dataset could improve the detection performance.Comment: 8 pages, 7 figures, accepted in MICCAI 2018 Breast Image Analysis
(BIA
Breast Cancer Detection Using Convolutional Neural Networks
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 automated
breast mass abnormality detection. Our model detects mass region and classifies
them into benign or malignant abnormality in mammogram(MG) images at once. For
the proposed model, MG images were collected from different hospitals,
locally.The images were passed through different preprocessing stages such as
gaussian filter, median filter, bilateral filters and extracted the region of
the breast from the background of the MG image. The performance of the model on
test dataset is found to be: detection accuracy 91.86%, sensitivity of 94.67%
and AUC-ROC of 92.2%
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
Quantitative assessment of Tumor-TIL spatial relationships is increasingly
important in both basic science and clinical aspects of breast cancer research.
We have developed and evaluated convolutional neural network (CNN) analysis
pipelines to generate combined maps of cancer regions and tumor infiltrating
lymphocytes (TILs) in routine diagnostic breast cancer whole slide tissue
images (WSIs). We produce interactive whole slide maps that provide 1) insight
about the structural patterns and spatial distribution of lymphocytic
infiltrates and 2) facilitate improved quantification of TILs. We evaluated
both tumor and TIL analyses using three CNN networks - Resnet-34, VGG16 and
Inception v4, and demonstrated that the results compared favorably to those
obtained by what believe are the best published methods. We have produced
open-source tools and generated a public dataset consisting of tumor/TIL maps
for 1,015 TCGA breast cancer images. We also present a customized web-based
interface that enables easy visualization and interactive exploration of
high-resolution combined Tumor-TIL maps for 1,015TCGA invasive breast cancer
cases that can be downloaded for further downstream analyses.Comment: The American Journal of Patholog