From 58401e220a2dd5120b373eef40e9737d7ce8d56c Mon Sep 17 00:00:00 2001 From: Li Shen Date: Thu, 24 Feb 2022 14:00:58 -0500 Subject: [PATCH] Update README.md --- README.md | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index 197b63e..234f428 100644 --- a/README.md +++ b/README.md @@ -24,16 +24,16 @@ This is the companion site for our paper that was originally titled "End-to-end 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/open?id=0B1PVLadG_dCKV2pZem5MTjc1cHc). YaroslavNet is the DM challenge top-performing team's [method](https://www.synapse.org/#!Synapse:syn9773040/wiki/426908). Here is a table for individual 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 | Link | -|---|---|---|---|---|---| -| DDSM | Resnet50 | \[512-512-1024\]x2 | 0.86 | 0.88 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKSUJYdzNyZjVsZHc) | -| DDSM | VGG16 | 512x1 | 0.83 | 0.86 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKYnREWlJQZ2JaSDQ) | -| DDSM | VGG16 | \[512-512-1024\]x2 | 0.85 | 0.88 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKdVQzbDRLNTZ4TXM) | -| DDSM | YaroslavNet | heatmap + max pooling + FC16-8 + shortcut | 0.83 | 0.86 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKVk9RM1dMeTkwcTg) | -| INbreast | VGG16 | 512x1 | 0.92 | 0.94 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKN0ZxNFdCRWxHRFU) | -| INbreast | VGG16 | \[512-512-1024\]x2 | 0.95 | 0.96 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKUnQwYVhOd2NfQlk) | +| 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. @@ -41,17 +41,17 @@ A few best whole image models are available for downloading at this Google Drive - 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/open?id=0B1PVLadG_dCKV2pZem5MTjc1cHc). Here is a table for individual download: - -| Model | Train Set | Accuracy | Link | -|---|---|---|---| -| Resnet50 | S10 | 0.89 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKMTc2RGV1NGF6bm8) | -| VGG16 | S10 | 0.84 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKZUotelJBTzkwaGM) | -| VGG19 | S10 | 0.79 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKUHFpeTgwaVFYS1E) | -| YaroslavNet (Final) | S10 | 0.89 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKSlR4V1QwTGdsbGs) | -| Resnet50 | S30 | 0.91 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKSW1RZ0NzOVBJWHc) | -| VGG16 | S30 | 0.86 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKbjdodnp2SlR2WFU) | -| VGG19 | S30 | 0.89 | [download](https://drive.google.com/open?id=0B1PVLadG_dCKNUpGc3Q1dWJ5OGM) | +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.