================================================================= 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: [[1962 15 0 0 0 6 1 0 123] [ 0 0 0 0 0 0 0 0 0] [ 543 41 2133 8 0 35 0 7 366] [ 4 0 0 415 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 497 0 0 12] [ 0 0 0 0 0 0 726 0 20] [ 0 0 0 0 0 0 0 0 0] [ 81 49 41 5 0 8 2 6 2234]] Scoring Summary: (0) norm = 6.8818% (2) nneo = 31.9183% (3) infl = 0.9547% (5) dcis = 2.3576% (6) indc = 2.6810% (8) bckg = 7.9143% avg lbls = 8.9587% avg bckg = 7.9143% score = 8.8542% <** ================================================================= 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: [[1230 8 0 0 0 2 0 0 101] [ 0 0 0 0 0 0 0 0 0] [ 279 23 1202 2 0 13 0 1 296] [ 2 0 0 318 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 278 0 0 18] [ 0 0 0 0 0 0 293 0 9] [ 0 0 0 0 0 0 0 0 0] [ 84 20 39 2 0 4 5 2 1179]] Scoring Summary: (0) norm = 8.2774% (2) nneo = 33.8106% (3) infl = 0.6250% (5) dcis = 6.0811% (6) indc = 2.9801% (8) bckg = 11.6854% avg lbls = 10.3548% avg bckg = 11.6854% score = 10.4879% <** ================================================================= 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: [[ 937 6 177 18 0 56 24 0 131] [ 0 0 0 0 0 0 0 0 0] [ 941 10 549 102 0 280 61 19 251] [ 12 0 8 207 0 2 2 0 0] [ 0 0 0 0 0 0 0 0 0] [ 94 0 46 6 0 38 6 0 53] [ 87 0 80 56 0 196 33 7 5] [ 0 0 0 0 0 0 0 0 0] [ 93 30 15 3 0 2 3 3 1033]] Scoring Summary: (0) norm = 30.5411% (2) nneo = 75.1920% (3) infl = 10.3896% (5) dcis = 84.3621% (6) indc = 92.8879% (8) bckg = 12.6058% avg lbls = 58.6746% avg bckg = 12.6058% score = 54.0677% <** ======================================================================== ================================================================= 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: [[1577 7 212 11 68 22 98 28 84] [ 0 0 0 0 0 0 0 0 0] [ 525 14 1211 92 105 629 281 136 140] [ 0 0 2 392 3 13 8 1 0] [ 0 0 0 0 0 0 0 0 0] [ 5 1 6 0 0 481 11 0 5] [ 6 0 9 28 18 52 623 3 7] [ 0 0 0 0 0 0 0 0 0] [ 17 7 17 3 35 10 37 27 2273]] Scoring Summary: (0) norm = 25.1542% (2) nneo = 61.3470% (3) infl = 6.4439% (5) dcis = 5.5010% (6) indc = 16.4879% (8) bckg = 6.3067% avg lbls = 22.9868% avg bckg = 6.3067% score = 21.3188% <** ================================================================= 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: [[ 987 9 121 7 40 12 50 5 110] [ 0 0 0 0 0 0 0 0 0] [ 384 7 732 49 73 293 132 39 107] [ 0 0 3 302 3 5 7 0 0] [ 0 0 0 0 0 0 0 0 0] [ 3 0 13 0 0 272 1 3 4] [ 2 0 5 12 14 24 244 1 0] [ 0 0 0 0 0 0 0 0 0] [ 29 4 9 6 16 13 24 14 1220]] Scoring Summary: (0) norm = 26.3982% (2) nneo = 59.6916% (3) infl = 5.6250% (5) dcis = 8.1081% (6) indc = 19.2053% (8) bckg = 8.6142% avg lbls = 23.8056% avg bckg = 8.6142% score = 22.2865% <** ================================================================= 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: [[ 846 5 189 6 54 19 117 27 86] [ 0 0 0 0 0 0 0 0 0] [ 490 5 779 80 85 366 213 106 89] [ 1 0 0 210 6 4 8 2 0] [ 0 0 0 0 0 0 0 0 0] [ 3 0 42 1 9 141 34 0 13] [ 7 2 27 16 15 158 196 41 2] [ 0 0 0 0 0 0 0 0 0] [ 8 6 22 0 23 11 22 4 1086]] Scoring Summary: (0) norm = 37.2869% (2) nneo = 64.7989% (3) infl = 9.0909% (5) dcis = 41.9753% (6) indc = 57.7586% (8) bckg = 8.1218% avg lbls = 42.1821% avg bckg = 8.1218% score = 38.7761% <** ========================================================================