================================================================= hyp_rnf_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[1788 14 0 119 0 23 3 10 150] [ 0 0 0 0 0 0 0 0 0] [ 909 32 1131 425 0 176 32 71 357] [ 0 0 0 418 0 0 0 0 1] [ 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 500 0 0 9] [ 0 0 0 22 0 0 705 0 19] [ 0 0 0 0 0 0 0 0 0] [ 70 30 24 21 0 4 7 11 2259]] Scoring Summary: (0) norm = 15.1400% (2) nneo = 63.9004% (3) infl = 0.2387% (5) dcis = 1.7682% (6) indc = 5.4960% (8) bckg = 6.8838% avg lbls = 17.3086% avg bckg = 6.8838% score = 16.2662% <** ================================================================= hyp_rnf_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 897 7 25 132 0 50 65 4 161] [ 0 0 0 0 0 0 0 0 0] [ 702 16 102 333 0 184 107 62 310] [ 14 0 1 293 0 7 3 2 0] [ 0 0 0 0 0 0 0 0 0] [ 65 0 49 34 0 77 39 10 22] [ 71 0 18 100 0 55 36 7 15] [ 0 0 0 0 0 0 0 0 0] [ 114 15 36 13 0 6 6 8 1137]] Scoring Summary: (0) norm = 33.1096% (2) nneo = 94.3833% (3) infl = 8.4375% (5) dcis = 73.9865% (6) indc = 88.0795% (8) bckg = 14.8315% avg lbls = 59.5993% avg bckg = 14.8315% score = 55.1225% <** ================================================================= hyp_rnf_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 851 4 46 138 0 67 88 12 143] [ 0 0 0 0 0 0 0 0 0] [ 815 8 141 374 0 322 221 67 265] [ 7 0 0 216 0 4 3 1 0] [ 0 0 0 0 0 0 0 0 0] [ 85 0 33 13 0 36 15 8 53] [ 58 0 41 111 0 152 77 19 6] [ 0 0 0 0 0 0 0 0 0] [ 74 23 20 4 0 0 8 6 1047]] Scoring Summary: (0) norm = 36.9162% (2) nneo = 93.6286% (3) infl = 6.4935% (5) dcis = 85.1852% (6) indc = 83.4052% (8) bckg = 11.4213% avg lbls = 61.1257% avg bckg = 11.4213% score = 56.1553% <** ======================================================================== ================================================================= hyp_cnn_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[1927 17 93 0 2 2 10 3 53] [ 0 0 0 0 0 0 0 0 0] [ 759 40 2066 14 4 45 53 17 135] [ 1 0 13 402 0 0 3 0 0] [ 0 0 0 0 0 0 0 0 0] [ 9 0 32 1 1 460 3 2 1] [ 19 0 31 5 1 7 677 1 5] [ 0 0 0 0 0 0 0 0 0] [ 14 63 19 0 2 0 3 3 2322]] Scoring Summary: (0) norm = 8.5430% (2) nneo = 34.0568% (3) infl = 4.0573% (5) dcis = 9.6267% (6) indc = 9.2493% (8) bckg = 4.2869% avg lbls = 13.1066% avg bckg = 4.2869% score = 12.2246% <** ================================================================= hyp_cnn_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[1226 12 41 0 0 2 5 2 53] [ 0 0 0 0 0 0 0 0 0] [ 528 31 1111 3 3 13 30 13 84] [ 1 0 7 312 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 4 0 17 2 0 267 4 2 0] [ 5 0 10 1 0 1 284 0 1] [ 0 0 0 0 0 0 0 0 0] [ 16 23 10 1 0 0 1 1 1283]] Scoring Summary: (0) norm = 8.5757% (2) nneo = 38.8216% (3) infl = 2.5000% (5) dcis = 9.7973% (6) indc = 5.9603% (8) bckg = 3.8951% avg lbls = 13.1310% avg bckg = 3.8951% score = 12.2074% <** ================================================================= hyp_cnn_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 844 13 297 5 3 21 57 12 97] [ 0 0 0 0 0 0 0 0 0] [ 737 26 965 36 11 86 152 56 144] [ 6 0 30 178 0 2 11 4 0] [ 0 0 0 0 0 0 0 0 0] [ 54 2 106 5 1 15 12 4 44] [ 42 2 201 25 2 48 117 24 3] [ 0 0 0 0 0 0 0 0 0] [ 51 36 38 0 0 1 6 7 1043]] Scoring Summary: (0) norm = 37.4351% (2) nneo = 56.3940% (3) infl = 22.9437% (5) dcis = 93.8272% (6) indc = 74.7845% (8) bckg = 11.7597% avg lbls = 57.0769% avg bckg = 11.7597% score = 52.5452% <** ========================================================================