medical-2

Breast Tumour Classification

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in breast-tumour-classification-2

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PCam

Libraries

i

Use these libraries to find breast-tumour-classification-2 models and implementations

pytorch/vision

2 papers 15,236

Datasets

PCam

BCNB

Breast Lesion Detection in Ultrasound Videos (CVA-Net)

BreastClassifications4

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No subtasks available.

Most implemented papers

Densely Connected Convolutional Networks

Kilian Q. Weinberger, Zhuang Liu, Gao Huang•Wed Aug 24 2016

The Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion, and has several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.

41544

Content

Deci-AI/super-gradients

2 papers 4,259

osmr/imgclsmob

2 papers 2,910

yoshitomo-matsubara/torchdistill

2 papers 1,231

andreasveit/densenet-pytorch

2 papers 445

hycis/TensorGraph

2 papers 68

lab-midas/med_segmentation

2 papers 16

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Paper Graph

BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

F. M. Calisto, N. Nunes, J. Nascimento•Mon Apr 06 2020

The field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening is described and recommendations from the radiologists for guiding the future design of medical imaging interfaces are summarized.

44 0

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Rotation Equivariant CNNs for Digital Pathology

Jim Winkens, Taco Cohen, M. Welling, Bastiaan S. Veeling, J. Linmans•Thu Jun 07 2018

This work proposes a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection, and presents a novel derived dataset to enable principled comparison of machine learning models, in combination with an initial benchmark.

668 0

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Rotation Equivariant Vector Field Networks

D. Tuia, N. Komodakis, Diego Marcos, M. Volpi•Wed Dec 28 2016

The Rotation Equivariant Vector Field Networks (RotEqNet), a Convolutional Neural Network architecture encoding rotation equivariance, invariance and covariance, is proposed and a modified convolution operator relying on this representation to obtain deep architectures is developed.

284 0

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Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images

N. Rajpoot, S. Graham, David B. A. Epstein•Sun Apr 05 2020

Dense Steerable Filter CNNs (DSF-CNNs) that use group convolutions with multiple rotated copies of each filter in a densely connected framework are proposed that achieve state-of-the-art performance, with significantly fewer parameters, when applied to three different tasks in the area of computational pathology.

96 0

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Group Equivariant Convolutional Networks

Taco Cohen, M. Welling•Tue Feb 23 2016

Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries and achieves state of the art results on CI- FAR10 and rotated MNIST.

2192 0

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Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis

M. Veta, J. Pluim, Maxime W. Lafarge, E. Bekkers, R. Duits•Wed Feb 19 2020

A framework to encode the geometric structure of the special Euclidean motion group SE(2) in convolutional networks to yield translation and rotation equivariance via the introduction of SE( 2)-group convolution layers is proposed.

85 0

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Meta-repository of screening mammography classifiers

Krzysztof J. Geras, Kyunghyun Cho, Benjamin Stadnick, J. Witowski, Vishwaesh Rajiv, Jakub Chledowski, Farah E. Shamout•Mon Aug 09 2021

A meta-repository containing models for classification of screening mammograms with open-source implementations and cross-platform compatibility, which creates a framework that enables the evaluation of AI models on any screening mammography data set.

11 0

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Multi-View Hypercomplex Learning for Breast Cancer Screening

D. Comminiello, E. Lopez, Eleonora Grassucci•Mon Apr 11 2022

Multi-view hypercomplex learning is introduced, a novel learning paradigm for multi-view breast cancer classification based on parameterized hypercomplex neural networks (PHNNs) that consistently outperforms state-of-the-art multi-view models, while also generalizing across radiographic modalities and tasks such as disease classification from chest X-rays and multimodal brain tumor segmentation.

10 0

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