Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
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.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationMedical Image AnalysisSimilar Papers 제목 키워드 기반
Label Stability in Multiple Instance Learning
We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers. These classifiers are trained only on globally annotated images (bags), but often can provide fine-grained annota…
Medical Image AnalysisMultiple Instance LearningAn interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be app…
Breast Cancer DetectionGPULesion SegmentationMedical Diagnosis+2Ensemble classifier approach in breast cancer detection and malignancy grading- A review
The diagnosed cases of Breast cancer is increasing annually and unfortunately getting converted into a high mortality rate. Cancer, at the early stages, is hard to detect because the malicious cells show similar properti…
BIG-bench Machine LearningBreast Cancer DetectionGeneral ClassificationWeakly Supervised Clustering by Exploiting Unique Class Count
A weakly supervised learning based clustering framework is proposed in this paper. As the core of this framework, we introduce a novel multiple instance learning task based on a bag level label called unique class count …
ClusteringMultiple Instance LearningSemantic SegmentationWeakly-supervised Learning+1An efficient deep neural network to find small objects in large 3D images
3D imaging enables accurate diagnosis by providing spatial information about organ anatomy. However, using 3D images to train AI models is computationally challenging because they consist of 10x or 100x more pixels than …
AnatomyGPU