{"task":"Cancer-no cancer per image classification","dataset":"CBIS-DDSM","metric_names":["AUC"],"rows":[{"id":135451,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"Multi-patch size DenseNet-121","metrics":{"AUC":"0.809"},"paper_url":"https://www.mdpi.com/2306-5354/10/5/534","paper_title":"Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification","paper_date":"2023-04-21","code_links":[],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135452,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"SingleView_PatchBased_EfficientNet-B0","metrics":{"AUC":"0.8033"},"paper_url":"https://arxiv.org/abs/2110.01606v3","paper_title":"Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network","paper_date":"2021-10-01","code_links":[{"title":"dpetrini/two-views-classifier","url":"https://github.com/dpetrini/two-views-classifier"}],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135453,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"MorphHR-ResNet18_S896","metrics":{"AUC":"0.7964"},"paper_url":"https://arxiv.org/abs/2101.07945v1","paper_title":"Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations","paper_date":"2021-01-20","code_links":[],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135454,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"ResNet18_S896","metrics":{"AUC":"0.7958"},"paper_url":"https://arxiv.org/abs/2101.07945v1","paper_title":"Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations","paper_date":"2021-01-20","code_links":[],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135455,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"SingleView_PatchBased_EfficientNet-B3","metrics":{"AUC":"0.7952"},"paper_url":"https://arxiv.org/abs/2110.01606v3","paper_title":"Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network","paper_date":"2021-10-01","code_links":[{"title":"dpetrini/two-views-classifier","url":"https://github.com/dpetrini/two-views-classifier"}],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135456,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"Multi-resolution DenseNet-121","metrics":{"AUC":"0.789"},"paper_url":"https://www.mdpi.com/2306-5354/10/5/534","paper_title":"Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification","paper_date":"2023-04-21","code_links":[],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135457,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"ResNet18_S448","metrics":{"AUC":"0.7882"},"paper_url":"https://arxiv.org/abs/2101.07945v1","paper_title":"Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations","paper_date":"2021-01-20","code_links":[],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135458,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"Feature Pyramid Network DenseNet-121","metrics":{"AUC":"0.788"},"paper_url":"https://www.mdpi.com/2306-5354/10/5/534","paper_title":"Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification","paper_date":"2023-04-21","code_links":[],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135459,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"Patch-based DenseNet-121","metrics":{"AUC":"0.784"},"paper_url":"https://www.mdpi.com/2306-5354/10/5/534","paper_title":"Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification","paper_date":"2023-04-21","code_links":[],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135460,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"MorphHR-ResNet18_S448","metrics":{"AUC":"0.7836"},"paper_url":"https://arxiv.org/abs/2101.07945v1","paper_title":"Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations","paper_date":"2021-01-20","code_links":[],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135461,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"MorphHR-ResNet18_S224","metrics":{"AUC":"0.7523"},"paper_url":"https://arxiv.org/abs/2101.07945v1","paper_title":"Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations","paper_date":"2021-01-20","code_links":[],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135462,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"VGG/ResNet","metrics":{"AUC":"0.75"},"paper_url":"http://arxiv.org/abs/1708.09427v5","paper_title":"Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography","paper_date":"2017-08-30","code_links":[{"title":"lishen/end2end-all-conv","url":"https://github.com/lishen/end2end-all-conv"},{"title":"nyukat/mammography_metarepository","url":"https://github.com/nyukat/mammography_metarepository"},{"title":"yuyuyu123456/CBIS-DDSM","url":"https://github.com/yuyuyu123456/CBIS-DDSM"},{"title":"gkaposto/end2end_lishen","url":"https://github.com/gkaposto/end2end_lishen"},{"title":"aralab-unr/ga-mammograms","url":"https://github.com/aralab-unr/ga-mammograms"}],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135463,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"VGG/ResNet","metrics":{"AUC":"0.75"},"paper_url":"https://arxiv.org/abs/2110.01606v3","paper_title":"Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network","paper_date":"2021-10-01","code_links":[{"title":"dpetrini/two-views-classifier","url":"https://github.com/dpetrini/two-views-classifier"}],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135464,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"ResNet18_S224","metrics":{"AUC":"0.7257"},"paper_url":"https://arxiv.org/abs/2101.07945v1","paper_title":"Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations","paper_date":"2021-01-20","code_links":[],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135465,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"XGBoost","metrics":{"AUC":"0.6849"},"paper_url":"https://pdfs.semanticscholar.org/9131/a8fe454ece5c207a41c9e594b3435f08824c.pdf","paper_title":"Machine Learning Algorithms for Breast Cancer Detection in Mammography Images: A Comparative Study","paper_date":"2021-04-26","code_links":[],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]},{"id":135466,"task":"Cancer-no cancer per image classification","parent_task":"Binary Classification","dataset":"CBIS-DDSM","model_name":"VGG16","metrics":{"AUC":"0.6822"},"paper_url":"https://pdfs.semanticscholar.org/9131/a8fe454ece5c207a41c9e594b3435f08824c.pdf","paper_title":"Machine Learning Algorithms for Breast Cancer Detection in Mammography Images: A Comparative Study","paper_date":"2021-04-26","code_links":[],"metrics_order":"[\"AUC\"]","area":"Natural Language Processing","uses_additional_data":0,"source":"archive","tags":[]}]}