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SotA/Methodology/Multi-Label Classification/CheXpert

Multi-Label Classification on CheXpert

Metric: AVERAGE AUC ON 14 LABEL (higher is better)

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#Model↕AVERAGE AUC ON 14 LABEL▼AugmentationsPaperDate↕Code
1CFT (ensemble) Macao Polytechnic University0.933No--Code
2DeepAUC-v10.93NoLarge-scale Robust Deep AUC Maximization: A New ...2020-12-06Code
3Hierarchical-Learning-V1 (ensemble)0.93NoInterpreting chest X-rays via CNNs that exploit ...2019-11-15Code
4YWW(ensemble)0.929No---
5Conditional-Training-LSR0.929No---
6Hierarchical-Learning-V4 (ensemble)0.929NoInterpreting chest X-rays via CNNs that exploit ...2019-11-15Code
7Conditional-Training-LSR-V10.929No---
8Hierarchical-Learning-V0 (ensemble)0.929No---
9Multi-Stage-Learning-CNN-V3 (ensemble)0.928No---
10DeepCNNsGM(ensemble)0.928No---
11inisis0.927No---
12DeepCNNs(ensemble)0.927No---
13SenseXDR0.927No---
14ihil (ensemble)0.927No---
15JF aboy ensemble_V2 JF HEALTHCARE https://github.com/deadpoppy/CheXpert-Challeng0.926No---
16DRNet (ensemble)0.926No---
17yw0.926No---
18Anatomy-XNet-V10.926NoAnatomy-XNet: An Anatomy Aware Convolutional Neu...2021-06-10-
19hoanganh_VB_ensemble30.925No---
20alimebkovk0.925No---
21uest0.924No---
22Hoang_VB_ensemble31_v0.924No---
23tedtt0.924No---
24as-hust-v30.924No---
25hoanganh_VB_VN0.924No---
26Hierarchical-CNN-Ensemble-V1 (ensemble)0.924No---
27DE_APR ensemble ltt0.923No---
28DE_APR_N ensemble ltt0.923No---
29Multi-Stage-Learning-CNN-V2 (ensemble)0.923No---
30Weighted-CNN(ensemble)0.923No---
31hoanganhcnu_ensemble27_v0.923No---
32YJ&&YWW :https://github.com/inisis/chexper0.923No---
33as-hust-v10.923No---
34Maxium (ensemble)0.923No---
35as-hust-v20.922No---
36Average-CNN(ensemble)0.922No---
37MaxAUC0.922No---
38zjr(ensembel)0.921No---
39SuperCNNv30.921No---
40hyc0.921No---
41hoangnguyenkcv10.921No---
42{"submit_id": "0x3c7b0af1b5784c159daf259c58543aa3", "predict_id": "0x67b23473183f4f43afa3b37edbc5d7fe", "submitter_id": "0x30db016ad564455ba055eb7f7f4402ac"0.92No---
43HOANG_VB_VN_20.92No---
44BDNB0.919No---
45JF Coolver ensemble0.919No---
46thang ensemble colo0.919No---
47hoangnn9 ensemble VBV0.919No---
48JF aboy ensemble_V1 JF HEALTHCARE https://github.com/deadpoppy/CheXpert-Challeng0.919No---
49{"submit_id": "0x33aeb0f2525e482a886196c273bdf1ba", "predict_id": "0xff2f60907da8440d98ff17f0af749535", "submitter_id": "0x0b382a226d4548c9b441f19b1907fe0f"0.919No---
50brian-baseline-v20.919No---
51DE_JUN4_RS_EN ensemble LTT0.918No---
52Mehdi_You (ensemble)0.918No---
53A Good Model (single model) Macao Polytechnic University0.918No--Code
54A Good Model (single model)0.918No---
55Anatomy-XNet (ensemble)0.917NoAnatomy-XNet: An Anatomy Aware Convolutional Neu...2021-06-10-
56Ensemble_v20.917No---
57Deep-CNNs-V10.917No---
58vdn6 ensemble ltt0.917No---
59Overfit ensemble OTH-A0.917No---
60thangbk(ensemble)0.917No---
61desmond0.916No---
62DE_JUN1_RS_EN ensemble LTT0.916No---
63DE_JUN3_RS_EN ensemble LTT0.916No---
64{"submit_id": "0x57dc2989f0474ca095d0841df09cfb18", "predict_id": "0xd43bcf7d4c9b467894db2b274b18794e", "submitter_id": "0x30db016ad564455ba055eb7f7f4402ac"0.916No---
65ATT-AW-v10.916No---
66{"submit_id": "0xeb9c9e79ed9e4410a2a37d62322f4585", "predict_id": "0x735b718280b14e83895decbc31641f87", "submitter_id": "0x30db016ad564455ba055eb7f7f4402ac"0.916No---
67Multi-Stage-Learning-CNN-V00.916No---
68TGNB0.915No---
69ensemble SN0.915No---
70zhangjingyan0.915No---
71Deadpoppy Ensemble0.915No---
72hoangnguyenkcv-ensemble280.915No---
73DE_JUN2_RS_EN ensemble LTT0.914No---
74GRNB0.914No---
75Deep-CNNs (ensemble)0.914No---
76Sky-Model0.913No---
77JF Deadpoppy0.913No---
78YWW-YJ:https://github.com/inisis/chexper0.913No---
79zjy0.912No---
80WL_Baseline (ensemble)0.912No---
81KCV-CNN-ensemble-CN0.911No---
82songta0.911No---
83bhtrun0.911No---
84anatomy_xnet_v1 (single model)0.911No---
85DS_APR_N single model ltt0.911No---
86DS_APR single model LTT0.911No---
87brian-baseline0.911No---
88ensemble SNU0.91No---
89HinaNetV2 (ensemble)0.909No---
90KD-Prune10 (Single model)0.909No---
91G_Mans_ensembl0.909No---
92Masks and Manuscripts0.909NoMasks and Manuscripts: Advancing Medical Pre-tra...2024-07-23-
93guran_ri0.908No---
94vdnnn (ensemble)0.908No---
95BAAZT0.908No---
96Stanford Baseline (ensemble)0.907NoCheXpert: A Large Chest Radiograph Dataset with ...2019-01-21Code
97vbn (single model)0.907No---
98muti_base (ensemble)0.907No---
99Z_Ensemble_V0.907No---
100{ForwardModelEnsembleCorrected} (ensemble)0.906No---
101LBC-v2 (ensemble)0.906NoImage Projective Transformation Rectification wi...2022-10-12Code
102LBC-v20.906No---
103LBC-v2 (ensemble)0.906NoImage Projective Transformation Rectification wi...2022-10-12Code
104Multi-CNN0.905No---
105hy0.905No---
106ForwardMECorrectedFull (ensemble)0.905No---
107JustAnotherDensenet0.904No---
108Orlando (single model)0.903No---
109Max (single model)0.902No---
110DeepLungsEnsemble0.902No---
111Ensemble_v10.901No---
112Nakajima_ayas0.901No---
113MLC11 NotDense (single-model)0.9No---
114vn_2 single_model ltt0.9No---
115{AVG_MAX}(ensemble)0.899No---
116Z_Ensemble_0.899No---
117llllldz0.899No---
118DiseaseNet Samg2003 single model, UIUC, http://sambhavgupta.com0.899No---
119DiseaseNet Samg2003 single model, DPS RKP, http://sambhavgupta.co0.899No---
120LBC-v00.899No---
121LBC-v0 (ensemble)0.899NoImage Projective Transformation Rectification wi...2022-10-12Code
122LBC-v0 (ensemble)0.899NoImage Projective Transformation Rectification wi...2022-10-12Code
123BUA0.898No---
124G_Mans_v2 (single model): LibAUC + coat_mini0.898No---
125ljc2260.898No---
126ForwardModelEnsemble (ensemble)0.897No---
127NewTrickTest (ensemble)0.897No---
128AccidentNet v1 (single model)0.897No---
129ylz-v010.896No---
130ldz0.896No---
131Densenet0.896No---
132Stellarium-CheXpert-Local (single model)0.896No---
133Stellarium-CheXpert-Local0.896NoImage Projective Transformation Rectification wi...2022-10-12Code
134Stellarium-CheXpert-Local0.896NoImage Projective Transformation Rectification wi...2022-10-12Code
135Deadpoppy Single0.895No---
136adoudo0.895No---
137{koala-large} (single model)0.895No---
138MVD1210.895No---
139hust(single model)0.895No---
140MM10.894No---
141hycN0.894No---
142zhujier0.894No---
143U-Random-Ind (single)0.894No---
144HybridModelEnsemble (ensemble)0.892No---
145MVD121-3200.891No---
146ylz-v020.891No---
147pause0.89No---
148Overfit ensemble OT0.89No---
149Haruka_Hamasak0.89No---
150DenseNet169 at 320x320 (single model)0.889No---
151LR-baseline (ensemble)0.889No---
152DataAugFTW (single model)0.888No---
153{koala} (single model)0.888No---
154Xception (single model)0.887No---
155Stellarium (single model)0.887No---
156Stellarium0.887No---
157pm_rn50_0.15pp0.887No---
158baseline30.886No---
159PrateekMunja0.886No---
160MVR500.886No---
161MNet-Fix (Single Model)0.884No---
162Coolver XH0.884No---
163Naive Densenet0.883No---
164mhealth_buet (single model)0.883No---
165Aoitori (single model)0.882No---
166{chexpert-classifier}(single model)0.882No---
167DearBrave (single model)0.882No---
168AccidentNet V2 (single model)0.881No---
169{densenet} (single model)0.88No---
170Yoake (single model)0.879No---
171MLC11 Baseline (single-model)0.878No---
172DenseNet0.876No---
173HCL1 (single model)0.876No---
174MLGCN (single model)0.875No---
175GCN_densenet121-single mode0.875No---
176GreenTeaCalpis (single model)0.873No---
177Multi-CNN (ensemble)0.873No---
178BASELINE ResNet500.871No---
179baseline1 (single model)0.868No---
180Baseline DenseNet1610.868No---
181DSENet0.865No---
182Densenet-Basic Single NUS0.863No---
183KD_Mobilenet (single model)0.862No---
184{GoDense} (single model)0.861No---
185inceptionv3_single_NN0.861No---
186MLKD (Single model)0.86No---
187BASELINE Acorn0.86No---
188ErrorNet (single model)0.859No---
189SleepNet (single model)0.859No---
190baseline20.858No---
191UMLS_CLIP (single model)0.858No---
192haw02 (single model)0.854No---
193CombinedTrainDenseNet121 (single model)0.853No---
194rayOfLightSingle (Single Model)0.851No---
195Model_Team_34 (single model)0.85No---
196Test model habbe0.85No---
197model2_DenseNet1210.848No---
198Baseline0.848No---
199HinaNet (single model)0.844No---
200singlehead_models (single model combined)0.842No---
201mwowra-conditional (single)0.84No---
202multihead_model (one model for all pathologies)0.838No---
203mobilenet (single model)0.837No---
204Grp12BigCNN0.835No---
205MLC9_Densenet (single model)0.834No---
206Grp12v2USup2OSamp (ensemble)0.83No---
207DNET121-single0.822No---
208DensNet1210.805NoCheXclusion: Fairness gaps in deep chest X-ray c...2020-02-14Code
209G_Mans_v1 (single model):0.797No---
21012ASLv2(single)0.769No---
211DenseNet121 (single model)0.76No---
21212ASLv1(single)0.736No---
213haw-baseline (single model)0.732No---
214rayOfLight (ensemble)0.727No---
215BASELINE DenseNet1210.724No---
216Chest-x-ray classification using0.618No---
217BME_Final_v20.615No---
218{densenet121}{single model0.606No---
219autobot0.606No---
220{MLC02_DenseNet121}0.575No---
221efficiantB5 (single model)0.531No---
222apalepu10.524No---
223Erdem (single)0.5No---
224Adalab Standard (Single Model)0.481No---
225Adalab Standard (single model)0.481No---
226zeroshot_medclip_baseline (ensemble)0.479No---