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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep Learning to Improve Breast Cancer Detection on Screening Mammography
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Shield: [![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa] This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png [cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg # Deep Learning to Improve Breast Cancer Detection on Screening Mammography (End-to-end Training for Whole Image Breast Cancer Screening using An All Convolutional Design) Li Shen, Ph.D. CS Icahn School of Medicine at Mount Sinai New York, New York, USA  ## Introduction This is the companion site for our paper that was originally titled "End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design" and was retitled as "Deep Learning to Improve Breast Cancer Detection on Screening Mammography". The paper has been published [here](https://rdcu.be/bPOYf). You may also find the arXiv version [here](https://arxiv.org/abs/1708.09427). This work was initially presented at the NIPS17 workshop on machine learning for health. Access the 4-page short paper [here](https://arxiv.org/abs/1711.05775). Download the [poster](https://raw.githubusercontent.com/lishen/end2end-all-conv/master/ddsm_train/NIPS17%20ML4H%20Poster.pdf). For our entry in the DREAM2016 Digital Mammography challenge, see this [write-up](https://www.synapse.org/LiShenDMChallenge). This work is much improved from our method used in the challenge. ## Whole image model downloads A few best whole image models are available for downloading at this Google Drive [folder](https://drive.google.com/drive/folders/0B1PVLadG_dCKV2pZem5MTjc1cHc?resourcekey=0-t4vtopuv27D9NnMC97w6hg&usp=sharing). YaroslavNet is the DM challenge top-performing team's [method](https://www.synapse.org/#!Synapse:syn9773040/wiki/426908). Here is a table for model AUCs: | Database | Patch Classifier | Top Layers (two blocks) | Single AUC | Augmented AUC | |---|---|---|---|---| | DDSM | Resnet50 | \[512-512-1024\]x2 | 0.86 | 0.88 | | DDSM | VGG16 | 512x1 | 0.83 | 0.86 | | DDSM | VGG16 | \[512-512-1024\]x2 | 0.85 | 0.88 | | DDSM | YaroslavNet | heatmap + max pooling + FC16-8 + shortcut | 0.83 | 0.86 | | INbreast | VGG16 | 512x1 | 0.92 | 0.94 | | INbreast | VGG16 | \[512-512-1024\]x2 | 0.95 | 0.96 | - Inference level augmentation is obtained by horizontal and vertical flips to generate 4 predictions. - The listed scores are single model AUC and prediction averaged AUC. - 3 Model averaging on DDSM gives AUC of 0.91 - 2 Model averaging on INbreast gives AUC of 0.96. ## Patch classifier model downloads Several patch classifier models (i.e. patch state) are also available for downloading at this Google Drive [folder](https://drive.google.com/drive/folders/0B1PVLadG_dCKZDVNYWZ1bll0cFU?resourcekey=0-EU80p95OCgKqOZZbvJIN-w&usp=sharing). Here is a table for model acc: | Model | Train Set | Accuracy | |---|---|---| | Resnet50 | S10 | 0.89 | | VGG16 | S10 | 0.84 | | VGG19 | S10 | 0.79 | | YaroslavNet (Final) | S10 | 0.89 | | Resnet50 | S30 | 0.91 | | VGG16 | S30 | 0.86 | | VGG19 | S30 | 0.89 | With patch classifier models, you can convert them into any whole image classifier by adding convolutional, FC and heatmap layers on top and see for yourself. ## A bit explanation of this repository's file structure - The **.py** files under the root directory are Python modules to be imported. - You shall set the `PYTHONPATH` variable like this: `export PYTHONPATH=$PYTHONPATH:your_path_to_repos/end2end-all-conv` so that the Python modules can be imported. - The code for patch sampling, patch classifie
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:20ba127f6136bcd4, topic:deep-learning