Breast Tumour Classification
9 papers with code • 1 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Breast Tumour Classification
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Libraries
Use these libraries to find Breast Tumour Classification models and implementations
PCam
BCNB
Breast Lesion Detection in Ultrasound Videos (CVA-Net)
BreastClassifications4
Latest papers
Multi-View Hypercomplex Learning for Breast Cancer Screening
ispamm/phbreast
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To overcome such limitations, in this paper, we propose a methodological approach for multi-view breast cancer classification based on parameterized hypercomplex neural networks.
Meta-repository of screening mammography classifiers
Artificial intelligence (AI) is showing promise in improving clinical diagnosis.
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.
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear.
Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
tueimage/se2cnn
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This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.
Rotation Equivariant CNNs for Digital Pathology
We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection.
Rotation equivariant vector field networks
In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image.
Densely Connected Convolutional Networks
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
Group Equivariant Convolutional Networks
adambielski/pytorch-gconv-experiments
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We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries.
