Breast Cancer Detection
28 papers with code • 4 benchmarks • 7 datasets
Benchmarks
These leaderboards are used to track progress in Breast Cancer Detection
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Datasets
BRACS
CBIS-DDSM
BCI
BreakHis
BreastClassifications4
Latest papers
Memory-aware curriculum federated learning for breast cancer classification
ameliajimenez/curriculum-federated-learning
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Our curriculum controls the order of the training samples paying special attention to those that are forgotten after the deployment of the global model.
XBNet : An Extremely Boosted Neural Network
Neural networks have proved to be very robust at processing unstructured data like images, text, videos, and audio.
Machine Learning-Based Approaches For Breast Cancer Detection in Microwave Imaging
Microwave imaging is a promising detection tool for harmless and non-ionizing screening of breast cancer.
$\text{O}^2$PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection
Breast cancer is among the most deadly diseases, distressing mostly women worldwide.
Curvature-based Feature Selection with Application in Classifying Electronic Health Records
Disruptive technologies provides unparalleled opportunities to contribute to the identifications of many aspects in pervasive healthcare, from the adoption of the Internet of Things through to Machine Learning (ML) techniques.
Differences between human and machine perception in medical diagnosis
We compare the two with respect to their robustness to Gaussian low-pass filtering, performing a subgroup analysis on microcalcifications and soft tissue lesions.
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
Breast Cancer Detection Using Convolutional Neural Networks
Breast cancer is prevalent in Ethiopia that accounts 34% among women cancer patients.
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
nyukat/GMIC
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In this work, we extend the globally-aware multiple instance classifier, a framework we proposed to address these unique properties of medical images.
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
SBU-BMI/quip_cancer_segmentation
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Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research.
