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SotA/Methodology/Multi-Label Classification/CheXpert

Multi-Label Classification on CheXpert

Metric: NUM RADS BELOW CURVE (higher is better)

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#Model↕NUM RADS BELOW CURVE▼AugmentationsPaperDate↕Code
1inisis3No---
2JF aboy ensemble_V2 JF HEALTHCARE https://github.com/deadpoppy/CheXpert-Challeng3No---
3DeepAUC-v12.8NoLarge-scale Robust Deep AUC Maximization: A New ...2020-12-06Code
4YWW(ensemble)2.8No---
5as-hust-v22.8No---
6Hierarchical-Learning-V1 (ensemble)2.6NoInterpreting chest X-rays via CNNs that exploit ...2019-11-15Code
7Conditional-Training-LSR2.6No---
8Hierarchical-Learning-V4 (ensemble)2.6NoInterpreting chest X-rays via CNNs that exploit ...2019-11-15Code
9Conditional-Training-LSR-V12.6No---
10Hierarchical-Learning-V0 (ensemble)2.6No---
11Multi-Stage-Learning-CNN-V3 (ensemble)2.6No---
12DeepCNNsGM(ensemble)2.6No---
13DeepCNNs(ensemble)2.6No---
14SenseXDR2.6No---
15ihil (ensemble)2.6No---
16DRNet (ensemble)2.6No---
17yw2.6No---
18Anatomy-XNet-V12.6NoAnatomy-XNet: An Anatomy Aware Convolutional Neu...2021-06-10-
19uest2.6No---
20DE_APR ensemble ltt2.6No---
21DE_APR_N ensemble ltt2.6No---
22Multi-Stage-Learning-CNN-V2 (ensemble)2.6No---
23Weighted-CNN(ensemble)2.6No---
24zjr(ensembel)2.6No---
25{"submit_id": "0x3c7b0af1b5784c159daf259c58543aa3", "predict_id": "0x67b23473183f4f43afa3b37edbc5d7fe", "submitter_id": "0x30db016ad564455ba055eb7f7f4402ac"2.6No---
26BDNB2.6No---
27JF Coolver ensemble2.6No---
28DE_JUN4_RS_EN ensemble LTT2.6No---
29Mehdi_You (ensemble)2.6No---
30A Good Model (single model) Macao Polytechnic University2.6No--Code
31A Good Model (single model)2.6No---
32Anatomy-XNet (ensemble)2.6NoAnatomy-XNet: An Anatomy Aware Convolutional Neu...2021-06-10-
33desmond2.6No---
34DE_JUN1_RS_EN ensemble LTT2.6No---
35TGNB2.6No---
36DE_JUN2_RS_EN ensemble LTT2.6No---
37hoanganh_VB_ensemble32.4No---
38alimebkovk2.4No---
39Hoang_VB_ensemble31_v2.4No---
40tedtt2.4No---
41as-hust-v32.4No---
42hoanganh_VB_VN2.4No---
43Hierarchical-CNN-Ensemble-V1 (ensemble)2.4No---
44hoanganhcnu_ensemble27_v2.4No---
45YJ&&YWW :https://github.com/inisis/chexper2.4No---
46as-hust-v12.4No---
47Maxium (ensemble)2.4No---
48Average-CNN(ensemble)2.4No---
49MaxAUC2.4No---
50SuperCNNv32.4No---
51hyc2.4No---
52hoangnguyenkcv12.4No---
53HOANG_VB_VN_22.4No---
54thang ensemble colo2.4No---
55hoangnn9 ensemble VBV2.4No---
56JF aboy ensemble_V1 JF HEALTHCARE https://github.com/deadpoppy/CheXpert-Challeng2.4No---
57Ensemble_v22.4No---
58DE_JUN3_RS_EN ensemble LTT2.4No---
59{"submit_id": "0x57dc2989f0474ca095d0841df09cfb18", "predict_id": "0xd43bcf7d4c9b467894db2b274b18794e", "submitter_id": "0x30db016ad564455ba055eb7f7f4402ac"2.4No---
60ATT-AW-v12.4No---
61ensemble SN2.4No---
62zhangjingyan2.4No---
63GRNB2.4No---
64{"submit_id": "0x33aeb0f2525e482a886196c273bdf1ba", "predict_id": "0xff2f60907da8440d98ff17f0af749535", "submitter_id": "0x0b382a226d4548c9b441f19b1907fe0f"2.2No---
65brian-baseline-v22.2No---
66Deep-CNNs-V12.2No---
67vdn6 ensemble ltt2.2No---
68Overfit ensemble OTH-A2.2No---
69{"submit_id": "0xeb9c9e79ed9e4410a2a37d62322f4585", "predict_id": "0x735b718280b14e83895decbc31641f87", "submitter_id": "0x30db016ad564455ba055eb7f7f4402ac"2.2No---
70Multi-Stage-Learning-CNN-V02.2No---
71Deadpoppy Ensemble2.2No---
72hoangnguyenkcv-ensemble282.2No---
73Sky-Model2.2No---
74JF Deadpoppy2.2No---
75zjy2.2No---
76KCV-CNN-ensemble-CN2.2No---
77songta2.2No---
78bhtrun2.2No---
79anatomy_xnet_v1 (single model)2.2No---
80ensemble SNU2.2No---
81HinaNetV2 (ensemble)2.2No---
82thangbk(ensemble)2No---
83Deep-CNNs (ensemble)2No---
84YWW-YJ:https://github.com/inisis/chexper2No---
85WL_Baseline (ensemble)2No---
86DS_APR_N single model ltt2No---
87DS_APR single model LTT2No---
88brian-baseline2No---
89KD-Prune10 (Single model)2No---
90guran_ri2No---
91Multi-CNN2No---
92Max (single model)2No---
93{AVG_MAX}(ensemble)2No---
94G_Mans_ensembl1.8No---
95vdnnn (ensemble)1.8No---
96BAAZT1.8No---
97Stanford Baseline (ensemble)1.8NoCheXpert: A Large Chest Radiograph Dataset with ...2019-01-21Code
98hy1.8No---
99DeepLungsEnsemble1.8No---
100Z_Ensemble_1.8No---
101BUA1.8No---
102Deadpoppy Single1.8No---
103vbn (single model)1.6No---
104muti_base (ensemble)1.6No---
105{ForwardModelEnsembleCorrected} (ensemble)1.6No---
106LBC-v2 (ensemble)1.6NoImage Projective Transformation Rectification wi...2022-10-12Code
107LBC-v21.6No---
108LBC-v2 (ensemble)1.6NoImage Projective Transformation Rectification wi...2022-10-12Code
109ForwardMECorrectedFull (ensemble)1.6No---
110Orlando (single model)1.6No---
111Ensemble_v11.6No---
112MLC11 NotDense (single-model)1.6No---
113llllldz1.6No---
114DiseaseNet Samg2003 single model, UIUC, http://sambhavgupta.com1.6No---
115DiseaseNet Samg2003 single model, DPS RKP, http://sambhavgupta.co1.6No---
116ForwardModelEnsemble (ensemble)1.6No---
117NewTrickTest (ensemble)1.6No---
118ylz-v011.6No---
119adoudo1.6No---
120MM11.6No---
121hycN1.6No---
122zhujier1.6No---
123HybridModelEnsemble (ensemble)1.6No---
124MNet-Fix (Single Model)1.6No---
125Z_Ensemble_V1.4No---
126Nakajima_ayas1.4No---
127LBC-v01.4No---
128LBC-v0 (ensemble)1.4NoImage Projective Transformation Rectification wi...2022-10-12Code
129LBC-v0 (ensemble)1.4NoImage Projective Transformation Rectification wi...2022-10-12Code
130G_Mans_v2 (single model): LibAUC + coat_mini1.4No---
131ldz1.4No---
132Densenet1.4No---
133Stellarium-CheXpert-Local (single model)1.4No---
134Stellarium-CheXpert-Local1.4NoImage Projective Transformation Rectification wi...2022-10-12Code
135Stellarium-CheXpert-Local1.4NoImage Projective Transformation Rectification wi...2022-10-12Code
136{koala-large} (single model)1.4No---
137DenseNet169 at 320x320 (single model)1.4No---
138LR-baseline (ensemble)1.4No---
139JustAnotherDensenet1.2No---
140vn_2 single_model ltt1.2No---
141ljc2261.2No---
142AccidentNet v1 (single model)1.2No---
143MVD1211.2No---
144MVD121-3201.2No---
145Xception (single model)1.2No---
146Stellarium (single model)1.2No---
147Stellarium1.2No---
148pm_rn50_0.15pp1.2No---
149baseline31.2No---
150Naive Densenet1.2No---
151{densenet} (single model)1.2No---
152DenseNet1.2No---
153MLGCN (single model)1.2No---
154hust(single model)1No---
155U-Random-Ind (single)1No---
156ylz-v021No---
157pause1No---
158Overfit ensemble OT1No---
159DataAugFTW (single model)1No---
160{koala} (single model)1No---
161PrateekMunja1No---
162AccidentNet V2 (single model)1No---
163HCL1 (single model)1No---
164GCN_densenet121-single mode1No---
165{GoDense} (single model)1No---
166baseline21No---
167Haruka_Hamasak0.8No---
168MVR500.8No---
169Coolver XH0.8No---
170Aoitori (single model)0.8No---
171GreenTeaCalpis (single model)0.8No---
172baseline1 (single model)0.8No---
173Densenet-Basic Single NUS0.8No---
174KD_Mobilenet (single model)0.8No---
175MLKD (Single model)0.8No---
176haw02 (single model)0.8No---
177mhealth_buet (single model)0.6No---
178{chexpert-classifier}(single model)0.6No---
179Yoake (single model)0.6No---
180MLC11 Baseline (single-model)0.6No---
181BASELINE ResNet500.6No---
182Baseline DenseNet1610.6No---
183DSENet0.6No---
184BASELINE Acorn0.6No---
185ErrorNet (single model)0.6No---
186SleepNet (single model)0.6No---
187Model_Team_34 (single model)0.6No---
188model2_DenseNet1210.6No---
189G_Mans_v1 (single model):0.6No---
190haw-baseline (single model)0.6No---
191DearBrave (single model)0.4No---
192Multi-CNN (ensemble)0.4No---
193inceptionv3_single_NN0.4No---
194rayOfLightSingle (Single Model)0.4No---
195Test model habbe0.4No---
196HinaNet (single model)0.4No---
197mwowra-conditional (single)0.4No---
198multihead_model (one model for all pathologies)0.4No---
199MLC9_Densenet (single model)0.4No---
200Baseline0.2Nopyannote.audio: neural building blocks for speak...2019-11-04Code
201singlehead_models (single model combined)0.2No---
202mobilenet (single model)0.2No---
203Grp12v2USup2OSamp (ensemble)0.2No---
204Chest-x-ray classification using0.2No---
205UMLS_CLIP (single model)0No---
206CombinedTrainDenseNet121 (single model)0No---
207Grp12BigCNN0No---
208DNET121-single0No---
20912ASLv2(single)0No---
210DenseNet121 (single model)0No---
21112ASLv1(single)0No---
212rayOfLight (ensemble)0No---
213BASELINE DenseNet1210No---
214BME_Final_v20No---
215{densenet121}{single model0No---
216autobot0No---
217{MLC02_DenseNet121}0No---
218efficiantB5 (single model)0No---
219apalepu10No---
220Erdem (single)0No---
221Adalab Standard (Single Model)0No---
222Adalab Standard (single model)0No---
223zeroshot_medclip_baseline (ensemble)0No---