Multi Label Classification On Chexpert
Metriken
AVERAGE AUC ON 14 LABEL
NUM RADS BELOW CURVE
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
| Paper Title | |||
|---|---|---|---|
| CFT (ensemble) Macao Polytechnic University | 0.933 | - | Category-Wise Fine-Tuning for Image Multi-label Classification with Partial Labels |
| DeepAUC-v1 | 0.930 | 2.800 | Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification |
| Hierarchical-Learning-V1 (ensemble) | 0.930 | 2.600 | Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels |
| YWW(ensemble) | 0.929 | 2.800 | - |
| Conditional-Training-LSR-V1 | 0.929 | 2.600 | - |
| Conditional-Training-LSR | 0.929 | 2.600 | - |
| Hierarchical-Learning-V4 (ensemble) | 0.929 | 2.600 | Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels |
| Hierarchical-Learning-V0 (ensemble) | 0.929 | 2.600 | - |
| DeepCNNsGM(ensemble) | 0.928 | 2.600 | - |
| Multi-Stage-Learning-CNN-V3 (ensemble) | 0.928 | 2.600 | - |
| SenseXDR | 0.927 | 2.600 | - |
| ihil (ensemble) | 0.927 | 2.600 | - |
| inisis | 0.927 | 3.000 | - |
| DeepCNNs(ensemble) | 0.927 | 2.600 | - |
| yw | 0.926 | 2.600 | - |
| Anatomy-XNet-V1 | 0.926 | 2.600 | Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays |
| JF aboy ensemble_V2 JF HEALTHCARE https://github.com/deadpoppy/CheXpert-Challeng | 0.926 | 3.000 | - |
| DRNet (ensemble) | 0.926 | 2.600 | - |
| hoanganh_VB_ensemble3 | 0.925 | 2.400 | - |
| alimebkovk | 0.925 | 2.400 | - |
0 of 226 row(s) selected.