A Hypersensitive Breast Cancer Detector

01/23/2020
by   Stefano Pedemonte, et al.
12

Early detection of breast cancer through screening mammography yields a 20-35 serve the growing population of women seeking screening mammography. Although commercial computer aided detection (CADe) software has been available to radiologists for decades, it has failed to improve the interpretation of full-field digital mammography (FFDM) images due to its low sensitivity over the spectrum of findings. In this work, we leverage a large set of FFDM images with loose bounding boxes of mammographically significant findings to train a deep learning detector with extreme sensitivity. Building upon work from the Hourglass architecture, we train a model that produces segmentation-like images with high spatial resolution, with the aim of producing 2D Gaussian blobs centered on ground-truth boxes. We replace the pixel-wise L_2 norm with a weak-supervision loss designed to achieve high sensitivity, asymmetrically penalizing false positives and false negatives while softening the noise of the loose bounding boxes by permitting a tolerance in misaligned predictions. The resulting system achieves a sensitivity for malignant findings of 0.99 with only 4.8 false positive markers per image. When utilized in a CADe system, this model could enable a novel workflow where radiologists can focus their attention with trust on only the locations proposed by the model, expediting the interpretation process and bringing attention to potential findings that could otherwise have been missed. Due to its nearly perfect sensitivity, the proposed detector can also be used as a high-performance proposal generator in two-stage detection systems.

READ FULL TEXT

page 1

page 3

research
04/13/2022

A deep learning algorithm for reducing false positives in screening mammography

Screening mammography improves breast cancer outcomes by enabling early ...
research
12/23/2019

Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach

Breast cancer remains a global challenge, causing over 1 million deaths ...
research
03/01/2020

Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography

Deep learning object detection algorithm has been widely used in medical...
research
08/12/2013

Local image registration a comparison for bilateral registration mammography

Early tumor detection is key in reducing the number of breast cancer dea...
research
02/27/2017

Revealing Hidden Potentials of the q-Space Signal in Breast Cancer

Mammography screening for early detection of breast lesions currently su...
research
01/23/2020

Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis

Mammography-based screening has helped reduce the breast cancer mortalit...
research
08/11/2023

M M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector

Deep-learning-based object detection methods show promise for improving ...

Please sign up or login with your details

Forgot password? Click here to reset