Breast Cancer Detection
28 papers with code • 4 benchmarks • 7 datasets
Benchmarks
These leaderboards are used to track progress in Breast Cancer Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
Datasets
BRACS
CBIS-DDSM
BCI
BreakHis
BreastClassifications4
Most implemented papers
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.
Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography
We also demonstrate that a whole image classifier trained using our end-to-end approach on the DDSM digitized film mammograms can be transferred to INbreast FFDM images using only a subset of the INbreast data for fine-tuning and without further reliance on the availability of lesion annotations.
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
alexander-rakhlin/ICIAR2018
•
•
In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification.
High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks
In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images.
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
nyukat/breast_cancer_classifier
•
•
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200, 000 exams (over 1, 000, 000 images).
CEIMVEN: An Approach of Cutting Edge Implementation of Modified Versions of EfficientNet (V1-V2) Architecture for Breast Cancer Detection and Classification from Ultrasound Images
ac005sheekar/ceimven-breast
•
•
In this research, we focused mostly on our rigorous implementations and iterative result analysis of different cutting-edge modified versions of EfficientNet architectures namely EfficientNet-V1 (b0-b7) and EfficientNet-V2 (b0-b3) with ultrasound image, named as CEIMVEN.
Detecting and classifying lesions in mammograms with Deep Learning
In the last two decades Computer Aided Diagnostics (CAD) systems were developed to help radiologists analyze screening mammograms.
On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset
AFAgarap/wisconsin-breast-cancer
•
•
The hyper-parameters used for all the classifiers were manually assigned.
Conditional Infilling GANs for Data Augmentation in Mammogram Classification
Deep learning approaches to breast cancer detection in mammograms have recently shown promising results.
Regression Concept Vectors for Bidirectional Explanations in Histopathology
Explanations for deep neural network predictions in terms of domain-related concepts can be valuable in medical applications, where justifications are important for confidence in the decision-making.
