Update README.

README.md

@@ -1,8 +1,6 @@
 # LongTailCXR
 
-## **[WORK IN PROGRESS]**
-
-Code repository for **"Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study"** by Gregory Holste, Song Wang, Ziyu Jiang, Tommy C. Shen, Ronald D. Summers, Yifan Peng, and Zhangyang Wang. To be presented at [DALI 2022](https://dali-miccai.github.io/), a MICCAI workshop.
+Code repository for [**"Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study"**](https://arxiv.org/abs/2208.13365) by Gregory Holste, Song Wang, Ziyu Jiang, Tommy C. Shen, Ronald D. Summers, Yifan Peng, and Zhangyang Wang. To be presented at [DALI 2022](https://dali-miccai.github.io/), a MICCAI workshop.
 
 -----
 
@@ -20,23 +18,23 @@ Imaging exams, such as chest radiography, will yield a small set of common findi
 
 All trained model weights are available below. In the following table, best results are **bolded** and second-best results are <u>underlined</u>. See paper for full results (bAcc = [balanced accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score)).
 
-| Method | NIH-LT bAcc | MIMIC-CXR-LT bAcc | NIH-LT Weights | MIMIC-CXR-LT Weights |
+| Method | NIH-CXR-LT bAcc | MIMIC-CXR-LT bAcc | NIH-CXR-LT Weights | MIMIC-CXR-LT Weights |
 | :--- | :---: | :---: | :---: | :---: |
-| Softmax | 0.115 | 0.169 | link | link |
-| CB Softmax | 0.269 | 0.227 | link | link |
-| RW Softmax | 0.260 | 0.211 | link | link |
-| Focal Loss | 0.122 | 0.172 | link | link |
-| CB Focal Loss | 0.232 | 0.191 | link | link |
-| RW Focal Loss | 0.197 | 0.239 | link | link |
-| LDAM | 0.178 | 0.165 | link | link |
-| CB LDAM | 0.235 | 0.225 | link | link |
-| CB LDAM-DRW | 0.281 | 0.267 | link | link |
-| RW LDAM | 0.279 | 0.243 | link | link |
-| RW LDAM-DRW | <u>0.289</u> | <u>0.275</u> | link | link |
-| MixUp | 0.118 | 0.176 | link | link |
-| Balanced-MixUp | 0.155 | 0.168 | link | link |
-| Decoupling (cRT) | **0.294** | **0.296** | link | link |
-| Decoupling ($\tau$-norm) | 0.214 | 0.230 | link | link |
+| Softmax | 0.115 | 0.169 | [link](https://drive.google.com/file/d/1lzDBDwRRcKmYHaypyLc59MsbdSbOuq75/view?usp=sharing) | [link](https://drive.google.com/file/d/1iKMqNX_KvczyuJZAibmRSWU5cqKQ0psn/view?usp=sharing) |
+| CB Softmax | 0.269 | 0.227 | [link](https://drive.google.com/file/d/1m0Xt_COF8SY5ZxKo3qrpwBDBjTnZIfd7/view?usp=sharing) | [link](https://drive.google.com/file/d/1GDCWZ0J1GhGdEqcEP9Ubp42p4tz_w58b/view?usp=sharing) |
+| RW Softmax | 0.260 | 0.211 | [link](https://drive.google.com/file/d/1rvl_W3ZP6-059hevrP8FiRC3CM41DN65/view?usp=sharing) | [link](https://drive.google.com/file/d/1li4zP5-hCtazWVzC8Cp99o3hUU6t_PVv/view?usp=sharing) |
+| Focal Loss | 0.122 | 0.172 | [link](https://drive.google.com/file/d/1YuMcxv9d8H1rH-nP-ccMmrT3MXJb0SGQ/view?usp=sharing) | [link](https://drive.google.com/file/d/1OxnUQxjAfsrydXcaJ6Xy2-WlA7jNNkRG/view?usp=sharing) |
+| CB Focal Loss | 0.232 | 0.191 | [link](https://drive.google.com/file/d/1wOk9NlDrp4c52WjvJsqetxlEVfFJndBr/view?usp=sharing) | [link](https://drive.google.com/file/d/1ZzPJTA-OBLYphkzO5yF8r_VZgpoa6tXT/view?usp=sharing) |
+| RW Focal Loss | 0.197 | 0.239 | [link](https://drive.google.com/file/d/1wMa6hd8J3jxlled7B66iDV43C3zdtL8l/view?usp=sharing) | [link](https://drive.google.com/file/d/1eTZ5K8HeDHzu3y_Nj0K2_MxPrK-E9MJg/view?usp=sharing) |
+| LDAM | 0.178 | 0.165 | [link](https://drive.google.com/file/d/1i_kXKI4IXbWyABk6ChsqkSAaRu_LkmCi/view?usp=sharing) | [link](https://drive.google.com/file/d/1eT16iWKrpxJNIghLdaSq9Hr4aAt99CAL/view?usp=sharing) |
+| CB LDAM | 0.235 | 0.225 | [link](https://drive.google.com/file/d/1p8uYrJH539Q9DgsEg7Ru_wOyRbZ1_taF/view?usp=sharing) | [link](https://drive.google.com/file/d/1mlOcyTuAN5SVlBXw-qyON7jXk7dHnhho/view?usp=sharing) |
+| CB LDAM-DRW | 0.281 | 0.267 | [link](https://drive.google.com/file/d/17HMaldk6pwHEHZ-c3SJwPw3JWeYjCtI6/view?usp=sharing) | [link](https://drive.google.com/file/d/1YUtJq5iPgbd4O_p77EhhXJoA_CfvR8Ct/view?usp=sharing) |
+| RW LDAM | 0.279 | 0.243 | [link](https://drive.google.com/file/d/1TZikaKB2sAqBA4o6bp9zVly463UAAftH/view?usp=sharing) | [link](https://drive.google.com/file/d/1X6p12_79o46OIBvSnnwERurv9x7eMf7t/view?usp=sharing) |
+| RW LDAM-DRW | <u>0.289</u> | <u>0.275</u> | [link](https://drive.google.com/file/d/1hVe7y4sWE0o90UsZSRraQAU0UEmcu73c/view?usp=sharing) | [link](https://drive.google.com/file/d/1OVHRGfQVia3SU5UTBoQ2FtcRkiaYK63E/view?usp=sharing) |
+| MixUp | 0.118 | 0.176 | [link](https://drive.google.com/file/d/1gP1LTgBQsrgCqzu3lyFK7TkcaPnSI-q7/view?usp=sharing) | [link](https://drive.google.com/file/d/1OjlkBsuumdvTtrUfBSGnCbONhXEk_cYf/view?usp=sharing) |
+| Balanced-MixUp | 0.155 | 0.168 | [link](https://drive.google.com/file/d/1_GQXraEbGVMVu5WpAN8k1YB74M5yTNcV/view?usp=sharing) | [link](https://drive.google.com/file/d/16xA335kGktjH-O8iu8821LJc279bKjMh/view?usp=sharing) |
+| Decoupling (cRT) | **0.294** | **0.296** | [link](https://drive.google.com/file/d/1nOqVEeZBmzyMM8fm46ziY6dqQHHcsAHm/view?usp=sharing) | [link](https://drive.google.com/file/d/1rbpyKxQsIGZbclMW0Fauxbt2TrxXoK8H/view?usp=sharing) |
+| Decoupling (tau-norm) | 0.214 | 0.230 | -- | -- |
 
 -----
 
@@ -46,7 +44,7 @@ To reproduce the results presented in this paper...
 1. Register to download the MIMIC-CXR dataset from https://physionet.org/content/mimic-cxr/2.0.0/, and download the NIH ChestXRay14 dataset from https://nihcc.app.box.com/v/ChestXray-NIHCC/.
 2. Install prerequisite packages with Anaconda: `conda env create -f lt_cxr.yml` and `conda activate lt_cxr`.
 3. Run all MIMIC-CXR-LT experiments: `bash run_mimic-cxr-lt_experiments.sh` (changing the `--data_dir` argument to your MIMIC-CXR path).
-4. Run all NIH-LT experiments: `bash run_nih-lt_experiments.sh` (changing the `--data_dir` argument to your NIH ChestXRay14 path).
+4. Run all NIH-LT experiments: `bash run_nih-cxr-lt_experiments.sh` (changing the `--data_dir` argument to your NIH ChestXRay14 path).
 
 Labels for the MIMIC-CXR-LT benchmark presented in this paper can be found in the `labels/` directory. Labels for NIH-LT are readily available upon request; for access, please email Dr. Ronald Summers ([email protected]) and Greg Holste ([email protected]). All experiments were conducted on a single NVIDIA RTX A6000 GPU.