Update README.md
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README.md
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@@ -4,6 +4,9 @@ language:
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- en
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library_name: pytorch
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tags:
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- biosignals
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- multimodal model
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- time-series
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@@ -23,7 +26,6 @@ datasets:
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- PTB-XL
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- CSN
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- HMC
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- WESAD
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metrics:
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- balanced_accuracy
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- roc_auc
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@@ -31,9 +33,10 @@ metrics:
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- weighted_f1
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- cohen_kappa
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model index:
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- name: PanLUNA
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results:
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- task:
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type: time-series-classification
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name: EEG Abnormality Detection
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dataset:
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- type: pr_auc
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value: 0.899
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name: AUC-PR
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---
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- en
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library_name: pytorch
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tags:
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- eeg
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- ppg
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- ecg
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- biosignals
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- multimodal model
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- time-series
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- PTB-XL
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- CSN
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- HMC
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metrics:
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- balanced_accuracy
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- roc_auc
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- weighted_f1
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- cohen_kappa
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model index:
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- name: PanLUNA
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results:
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- task:
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finetuning: Full
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type: time-series-classification
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name: EEG Abnormality Detection
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dataset:
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- type: pr_auc
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value: 0.899
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name: AUC-PR
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- task:
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finetuing: Full
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type: time-series-classification
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name: EEG Sleep Stage Classification
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dataset:
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type: HMC
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name: Haaglanden Medisch Centrum sleep staging database
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metrics:
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- type: balanced_accuracy
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value: 0.742
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name: Balanced Accuracy (%)
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- type: cohen_kappa
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value: 0.695
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name: Cohen's Kappa
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- type: weighted_f1
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value: 0.766
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name: Weighted F1
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- task:
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finetuning: Low-Rank Adaptation (LoRA)
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type: time-series-classification
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name: ECG PTB-XL Super Class
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dataset:
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type: PTB-XL
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name: PTB-XL, a large publicly available electrocardiography dataset
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metrics:
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- type: roc_auc
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value: 0.908
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name: AUROC
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- task:
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finetuning: Low-Rank Adaptation (LoRA)
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type: time-series-classification
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name: ECG PTB-XL Sub Class
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dataset:
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type: PTB-XL
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name: PTB-XL, a large publicly available electrocardiography dataset
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metrics:
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- type: roc_auc
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value: 0.888
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name: AUROC
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- task:
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finetuning: Low-Rank Adaptation (LoRA)
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type: time-series-classification
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name: ECG PTB-XL Form
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dataset:
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type: PTB-XL
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name: PTB-XL, a large publicly available electrocardiography dataset
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metrics:
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- type: roc_auc
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value: 0.833
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name: AUROC
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- task:
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finetuning: Low-Rank Adaptation (LoRA)
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type: time-series-classification
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name: ECG PTB-XL Rhythm
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dataset:
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type: PTB-XL
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name: PTB-XL, a large publicly available electrocardiography dataset
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metrics:
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- type: roc_auc
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value: 0.964
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name: AUROC
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- task:
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finetuning: Low-Rank Adaptation (LoRA)
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type: time-series-classification
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name: ECG CSN
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dataset:
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type: CSN
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name: Chapman-Shaoxing-Ningbo
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metrics:
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- type: roc_auc
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value: 0.950
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name: AUROC
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---
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