基于Swin-Unet自适应多尺度训练、多类别分割、迁移学习：心脏左心房冠状面分割【包含切片好的数据、代码、训练好的结果等等】

[train hyper-parameters: Namespace(batch_size=8, epochs=50, lr=0.005, lrf=0.01)] [epoch: 1] global correct: 0.9772 precision: ['0.9772', '0.2941'] recall: ['1.0000', '0.0001'] IoU: ['0.9772', '0.0001'] mean IoU: 0.4886 [epoch: 2] global correct: 0.9773 precision: ['0.9773', '0.7405'] recall: ['0.9999', '0.0064'] IoU: ['0.9773', '0.0064'] mean IoU: 0.4919 [epoch: 3] global correct: 0.9772 precision: ['0.9772', '1.0000'] recall: ['1.0000', '0.0002'] IoU: ['0.9772', '0.0002'] mean IoU: 0.4887 [epoch: 4] global correct: 0.9846 precision: ['0.9894', '0.7136'] recall: ['0.9949', '0.5412'] IoU: ['0.9844', '0.4446'] mean IoU: 0.7145 [epoch: 5] global correct: 0.9868 precision: ['0.9902', '0.7852'] recall: ['0.9963', '0.5789'] IoU: ['0.9866', '0.4998'] mean IoU: 0.7432 [epoch: 6] global correct: 0.9837 precision: ['0.9843', '0.9067'] recall: ['0.9992', '0.3156'] IoU: ['0.9835', '0.3057'] mean IoU: 0.6446 [epoch: 7] global correct: 0.9835 precision: ['0.9839', '0.9347'] recall: ['0.9995', '0.2968'] IoU: ['0.9834', '0.2908'] mean IoU: 0.6371 [epoch: 8] global correct: 0.9879 precision: ['0.9914', '0.7991'] recall: ['0.9963', '0.6277'] IoU: ['0.9877', '0.5421'] mean IoU: 0.7649 [epoch: 9] global correct: 0.9885 precision: ['0.9935', '0.7627'] recall: ['0.9948', '0.7211'] IoU: ['0.9883', '0.5890'] mean IoU: 0.7887 [epoch: 10] global correct: 0.9889 precision: ['0.9923', '0.8120'] recall: ['0.9964', '0.6695'] IoU: ['0.9888', '0.5797'] mean IoU: 0.7842 [epoch: 11] global correct: 0.9893 precision: ['0.9916', '0.8569'] recall: ['0.9975', '0.6356'] IoU: ['0.9891', '0.5747'] mean IoU: 0.7819 [epoch: 12] global correct: 0.9893 precision: ['0.9929', '0.8088'] recall: ['0.9962', '0.6948'] IoU: ['0.9891', '0.5968'] mean IoU: 0.7930 [epoch: 13] global correct: 0.9880 precision: ['0.9889', '0.9187'] recall: ['0.9989', '0.5185'] IoU: ['0.9878', '0.4958'] mean IoU: 0.7418 [epoch: 14] global correct: 0.9900 precision: ['0.9936', '0.8164'] recall: ['0.9962', '0.7237'] IoU: ['0.9898', '0.6224'] mean IoU: 0.8061 [epoch: 15] global correct: 0.9899 precision: ['0.9926', '0.8472'] recall: ['0.9971', '0.6799'] IoU: ['0.9897', '0.6056'] mean IoU: 0.7977 [epoch: 16] global correct: 0.9899 precision: ['0.9920', '0.8674'] recall: ['0.9977', '0.6572'] IoU: ['0.9897', '0.5972'] mean IoU: 0.7935 [epoch: 17] global correct: 0.9898 precision: ['0.9945', '0.7813'] recall: ['0.9950', '0.7645'] IoU: ['0.9896', '0.6298'] mean IoU: 0.8097 [epoch: 18] global correct: 0.9905 precision: ['0.9946', '0.8084'] recall: ['0.9958', '0.7665'] IoU: ['0.9904', '0.6487'] mean IoU: 0.8195 [epoch: 19] global correct: 0.9908 precision: ['0.9944', '0.8245'] recall: ['0.9962', '0.7587'] IoU: ['0.9907', '0.6532'] mean IoU: 0.8219 [epoch: 20] global correct: 0.9899 precision: ['0.9909', '0.9231'] recall: ['0.9988', '0.6063'] IoU: ['0.9897', '0.5772'] mean IoU: 0.7834 [epoch: 21] global correct: 0.9906 precision: ['0.9937', '0.8358'] recall: ['0.9967', '0.7312'] IoU: ['0.9904', '0.6394'] mean IoU: 0.8149 [epoch: 22] global correct: 0.9912 precision: ['0.9947', '0.8296'] recall: ['0.9963', '0.7739'] IoU: ['0.9911', '0.6678'] mean IoU: 0.8294 [epoch: 23] global correct: 0.9912 precision: ['0.9928', '0.8987'] recall: ['0.9982', '0.6902'] IoU: ['0.9910', '0.6404'] mean IoU: 0.8157 [epoch: 24] global correct: 0.9911 precision: ['0.9955', '0.8044'] recall: ['0.9954', '0.8077'] IoU: ['0.9910', '0.6751'] mean IoU: 0.8330 [epoch: 25] global correct: 0.9913 precision: ['0.9951', '0.8237'] recall: ['0.9961', '0.7895'] IoU: ['0.9912', '0.6754'] mean IoU: 0.8333 [epoch: 26] global correct: 0.9913 precision: ['0.9928', '0.9062'] recall: ['0.9983', '0.6892'] IoU: ['0.9911', '0.6433'] mean IoU: 0.8172 [epoch: 27] global correct: 0.9918 precision: ['0.9945', '0.8583'] recall: ['0.9971', '0.7648'] IoU: ['0.9916', '0.6791'] mean IoU: 0.8354 [epoch: 28] global correct: 0.9919 precision: ['0.9943', '0.8741'] recall: ['0.9975', '0.7546'] IoU: ['0.9918', '0.6807'] mean IoU: 0.8362 [epoch: 29] global correct: 0.9905 precision: ['0.9968', '0.7539'] recall: ['0.9934', '0.8638'] IoU: ['0.9903', '0.6738'] mean IoU: 0.8321 [epoch: 30] global correct: 0.9922 precision: ['0.9950', '0.8584'] recall: ['0.9970', '0.7860'] IoU: ['0.9920', '0.6958'] mean IoU: 0.8439 [epoch: 31] global correct: 0.9917 precision: ['0.9963', '0.8048'] recall: ['0.9952', '0.8399'] IoU: ['0.9915', '0.6977'] mean IoU: 0.8446 [epoch: 32] global correct: 0.9922 precision: ['0.9950', '0.8572'] recall: ['0.9969', '0.7871'] IoU: ['0.9920', '0.6959'] mean IoU: 0.8439 [epoch: 33] global correct: 0.9921 precision: ['0.9954', '0.8439'] recall: ['0.9965', '0.8028'] IoU: ['0.9920', '0.6990'] mean IoU: 0.8455 [epoch: 34] global correct: 0.9916 precision: ['0.9962', '0.8035'] recall: ['0.9952', '0.8374'] IoU: ['0.9915', '0.6951'] mean IoU: 0.8433 [epoch: 35] global correct: 0.9922 precision: ['0.9941', '0.8942'] recall: ['0.9979', '0.7473'] IoU: ['0.9921', '0.6866'] mean IoU: 0.8393 [epoch: 36] global correct: 0.9924 precision: ['0.9950', '0.8687'] recall: ['0.9972', '0.7845'] IoU: ['0.9922', '0.7013'] mean IoU: 0.8468 [epoch: 37] global correct: 0.9924 precision: ['0.9940', '0.9097'] recall: ['0.9983', '0.7422'] IoU: ['0.9923', '0.6913'] mean IoU: 0.8418 [epoch: 38] global correct: 0.9924 precision: ['0.9953', '0.8569'] recall: ['0.9969', '0.7990'] IoU: ['0.9922', '0.7049'] mean IoU: 0.8486 [epoch: 39] global correct: 0.9926 precision: ['0.9954', '0.8625'] recall: ['0.9970', '0.8027'] IoU: ['0.9924', '0.7116'] mean IoU: 0.8520 [epoch: 40] global correct: 0.9924 precision: ['0.9958', '0.8411'] recall: ['0.9964', '0.8195'] IoU: ['0.9922', '0.7096'] mean IoU: 0.8509 [epoch: 41] global correct: 0.9926 precision: ['0.9952', '0.8690'] recall: ['0.9972', '0.7960'] IoU: ['0.9925', '0.7107'] mean IoU: 0.8516 [epoch: 42] global correct: 0.9926 precision: ['0.9953', '0.8673'] recall: ['0.9971', '0.7997'] IoU: ['0.9925', '0.7125'] mean IoU: 0.8525 [epoch: 43] global correct: 0.9926 precision: ['0.9957', '0.8514'] recall: ['0.9967', '0.8166'] IoU: ['0.9924', '0.7147'] mean IoU: 0.8536 [epoch: 44] global correct: 0.9926 precision: ['0.9956', '0.8573'] recall: ['0.9968', '0.8120'] IoU: ['0.9925', '0.7154'] mean IoU: 0.8539 [epoch: 45] global correct: 0.9928 precision: ['0.9952', '0.8766'] recall: ['0.9974', '0.7940'] IoU: ['0.9926', '0.7142'] mean IoU: 0.8534 [epoch: 46] global correct: 0.9927 precision: ['0.9955', '0.8630'] recall: ['0.9970', '0.8070'] IoU: ['0.9925', '0.7154'] mean IoU: 0.8540 [epoch: 47] global correct: 0.9927 precision: ['0.9954', '0.8703'] recall: ['0.9972', '0.8011'] IoU: ['0.9926', '0.7157'] mean IoU: 0.8541 [epoch: 48] global correct: 0.9927 precision: ['0.9956', '0.8589'] recall: ['0.9969', '0.8115'] IoU: ['0.9925', '0.7161'] mean IoU: 0.8543 [epoch: 49] global correct: 0.9927 precision: ['0.9954', '0.8660'] recall: ['0.9971', '0.8046'] IoU: ['0.9926', '0.7155'] mean IoU: 0.8540 [epoch: 50] global correct: 0.9927 precision: ['0.9954', '0.8701'] recall: ['0.9972', '0.8010'] IoU: ['0.9926', '0.7154'] mean IoU: 0.8540