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自学教程：最新乳癌检测深度学习论文

51自学网 2020-01-28 13:23:44

学术与代码

这篇教程最新乳癌检测深度学习论文写得很实用，希望能帮到您。

Latest papers with code

Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer

26 May 2019 • SBU-BMI/quip_cancer_segmentation •

Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research.

BREAST CANCER DETECTION

26 May 2019

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Regression Concept Vectors for Bidirectional Explanations in Histopathology

9 Apr 2019 • medgift/iMIMIC-RCVs •

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.

BREAST CANCER DETECTION BREAST CANCER HISTOLOGY IMAGE CLASSIFICATION DECISION MAKING HISTOPATHOLOGICAL IMAGE CLASSIFICATION

09 Apr 2019

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Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

20 Mar 2019 • 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).

BREAST CANCER DETECTION

495

20 Mar 2019

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Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

2 Feb 2018 • alexander-rakhlin/ICIAR2018 •

In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification.

BREAST CANCER DETECTION BREAST CANCER HISTOLOGY IMAGE CLASSIFICATION HISTOPATHOLOGICAL IMAGE CLASSIFICATION IMAGE CLASSIFICATION

142

02 Feb 2018

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On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset

20 Nov 2017 • AFAgarap/wisconsin-breast-cancer •

The hyper-parameters used for all the classifiers were manually assigned.

BREAST CANCER DETECTION

20 Nov 2017

Paper

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Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography

30 Aug 2017 • lishen/end2end-all-conv •

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.

BREAST CANCER DETECTION

168

30 Aug 2017

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