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--- |
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language: en |
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license: mit |
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datasets: |
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- DDR |
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- FGADR |
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- IDRID |
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- MESSIDOR |
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- RETLES |
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library: torchSeg |
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model-index: |
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- name: unet_seresnext50_32x4d |
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results: |
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- task: |
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type: image-segmentation |
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dataset: |
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name: IDRID |
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type: IDRID |
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metrics: |
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- type: roc_auc |
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value: 0.6701094508171082 |
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name: AUC Precision Recall - IDRID COTTON_WOOL_SPOT - COTTON_WOOL_SPOT |
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- type: roc_auc |
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value: 0.7860875129699707 |
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name: AUC Precision Recall - IDRID EXUDATES - EXUDATES |
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- type: roc_auc |
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value: 0.6743975877761841 |
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name: AUC Precision Recall - IDRID HEMORRHAGES - HEMORRHAGES |
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- type: roc_auc |
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value: 0.39846163988113403 |
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name: AUC Precision Recall - IDRID MICROANEURYSMS - MICROANEURYSMS |
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- task: |
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type: image-segmentation |
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dataset: |
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name: FGADR |
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type: FGADR |
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metrics: |
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- type: roc_auc |
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value: 0.4449217915534973 |
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name: AUC Precision Recall - FGADR COTTON_WOOL_SPOT - COTTON_WOOL_SPOT |
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- type: roc_auc |
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value: 0.6951484084129333 |
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name: AUC Precision Recall - FGADR EXUDATES - EXUDATES |
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- type: roc_auc |
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value: 0.6508341431617737 |
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name: AUC Precision Recall - FGADR HEMORRHAGES - HEMORRHAGES |
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- type: roc_auc |
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value: 0.2895563244819641 |
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name: AUC Precision Recall - FGADR MICROANEURYSMS - MICROANEURYSMS |
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- task: |
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type: image-segmentation |
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dataset: |
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name: MESSIDOR |
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type: MESSIDOR |
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metrics: |
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- type: roc_auc |
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value: 0.3307325839996338 |
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name: AUC Precision Recall - MESSIDOR COTTON_WOOL_SPOT - COTTON_WOOL_SPOT |
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- type: roc_auc |
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value: 0.7123324871063232 |
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name: AUC Precision Recall - MESSIDOR EXUDATES - EXUDATES |
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- type: roc_auc |
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value: 0.3926454186439514 |
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name: AUC Precision Recall - MESSIDOR HEMORRHAGES - HEMORRHAGES |
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- type: roc_auc |
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value: 0.4098129868507385 |
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name: AUC Precision Recall - MESSIDOR MICROANEURYSMS - MICROANEURYSMS |
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- task: |
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type: image-segmentation |
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dataset: |
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name: DDR |
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type: DDR |
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metrics: |
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- type: roc_auc |
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value: 0.5084977746009827 |
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name: AUC Precision Recall - DDR COTTON_WOOL_SPOT - COTTON_WOOL_SPOT |
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- type: roc_auc |
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value: 0.6117375493049622 |
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name: AUC Precision Recall - DDR EXUDATES - EXUDATES |
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- type: roc_auc |
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value: 0.5447860956192017 |
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name: AUC Precision Recall - DDR HEMORRHAGES - HEMORRHAGES |
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- type: roc_auc |
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value: 0.23405438661575317 |
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name: AUC Precision Recall - DDR MICROANEURYSMS - MICROANEURYSMS |
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- task: |
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type: image-segmentation |
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dataset: |
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name: RETLES |
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type: RETLES |
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metrics: |
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- type: roc_auc |
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value: 0.5254419445991516 |
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name: AUC Precision Recall - RETLES COTTON_WOOL_SPOT - COTTON_WOOL_SPOT |
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- type: roc_auc |
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value: 0.7039055824279785 |
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name: AUC Precision Recall - RETLES EXUDATES - EXUDATES |
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- type: roc_auc |
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value: 0.5196094512939453 |
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name: AUC Precision Recall - RETLES HEMORRHAGES - HEMORRHAGES |
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- type: roc_auc |
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value: 0.4127877354621887 |
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name: AUC Precision Recall - RETLES MICROANEURYSMS - MICROANEURYSMS |
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--- |
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# Lesions Segmentation in Fundus |
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## Introduction |
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We focus on the semantic segmentations of: |
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1. Cotton Wool Spot |
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2. Exudates |
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3. Hemmorrhages |
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4. Microaneurysms |
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For an easier use of the models, we refer to cleaned-up version of the code provided in the [fundus lesions toolkit](https://github.com/ClementPla/fundus-lesions-toolkit/tree/main/). |
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## Architecture |
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The model uses unet_seresnext50_32x4d as architecture. The implementation is taken from [torchSeg](https://github.com/isaaccorley/torchseg) |
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## Training datasets |
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The model was trained on the following datasets: |
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DDR, FGADR, IDRID, MESSIDOR, RETLES |
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## Resolution |
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The image resolution for training was set to 1024 x 1024. |
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