Globally-Aware Multiple Instance Classifier for Breast Cancer Screening

06/07/2019
by   Yiqiu Shen, et al.
5

Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher resolutions and smaller regions of interest. Moreover, both the global structure and local details play important roles in medical image analysis tasks. To address these unique properties of medical images, we propose a neural network that is able to classify breast cancer lesions utilizing information from both a global saliency map and multiple local patches. The proposed model outperforms the ResNet-based baseline and achieves radiologist-level performance in the interpretation of screening mammography. Although our model is trained only with image-level labels, it is able to generate pixel-level saliency maps that provide localization of possible malignant findings.

READ FULL TEXT

page 5

page 7

page 10

page 11

research
02/13/2020

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

Medical images differ from natural images in significantly higher resolu...
research
12/16/2019

Zoom in to where it matters: a hierarchical graph based model for mammogram analysis

In clinical practice, human radiologists actually review medical images ...
research
03/21/2017

High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks

Recent advances in deep learning for natural images has prompted a surge...
research
03/23/2020

Understanding the robustness of deep neural network classifiers for breast cancer screening

Deep neural networks (DNNs) show promise in breast cancer screening, but...
research
02/22/2023

Magnification Invariant Medical Image Analysis: A Comparison of Convolutional Networks, Vision Transformers, and Token Mixers

Convolution Neural Networks (CNNs) are widely used in medical image anal...
research
08/08/2023

Few-shot medical image classification with simple shape and texture text descriptors using vision-language models

In this work, we investigate the usefulness of vision-language models (V...
research
10/16/2022

3D-GMIC: an efficient deep neural network to find small objects in large 3D images

3D imaging enables a more accurate diagnosis by providing spatial inform...

Please sign up or login with your details

Forgot password? Click here to reset