================================================================= 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: [[1798 14 0 76 0 35 28 9 147] [ 0 0 0 0 0 0 0 0 0] [ 994 34 681 337 0 279 366 68 374] [ 0 0 0 418 0 0 0 0 1] [ 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 496 0 0 13] [ 0 0 0 12 0 0 707 0 27] [ 0 0 0 0 0 0 0 0 0] [ 94 31 10 17 0 3 36 15 2220]] Scoring Summary: (0) norm = 14.6654% (2) nneo = 78.2636% (3) infl = 0.2387% (5) dcis = 2.5540% (6) indc = 5.2279% (8) bckg = 8.4913% avg lbls = 20.1899% avg bckg = 8.4913% score = 19.0201% <** ================================================================= 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: [[ 873 8 11 78 0 72 136 4 159] [ 0 0 0 0 0 0 0 0 0] [ 682 17 50 261 0 215 217 53 321] [ 8 0 0 287 0 10 11 3 1] [ 0 0 0 0 0 0 0 0 0] [ 67 0 14 28 0 80 75 10 22] [ 62 0 4 77 0 56 74 9 20] [ 0 0 0 0 0 0 0 0 0] [ 123 16 6 14 0 3 42 10 1121]] Scoring Summary: (0) norm = 34.8993% (2) nneo = 97.2467% (3) infl = 10.3125% (5) dcis = 72.9730% (6) indc = 75.4967% (8) bckg = 16.0300% avg lbls = 58.1856% avg bckg = 16.0300% score = 53.9701% <** ================================================================= 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: [[ 816 5 20 93 0 77 190 14 134] [ 0 0 0 0 0 0 0 0 0] [ 810 8 54 281 0 367 350 73 270] [ 4 0 0 216 0 4 6 1 0] [ 0 0 0 0 0 0 0 0 0] [ 84 0 7 11 0 35 43 9 54] [ 42 0 12 94 0 121 169 18 8] [ 0 0 0 0 0 0 0 0 0] [ 87 24 4 3 0 1 26 7 1030]] Scoring Summary: (0) norm = 39.5107% (2) nneo = 97.5599% (3) infl = 6.4935% (5) dcis = 85.5967% (6) indc = 63.5776% (8) bckg = 12.8596% avg lbls = 58.5477% avg bckg = 12.8596% score = 53.9789% <** ======================================================================== ================================================================= 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: [[1994 13 73 0 0 1 0 0 26] [ 0 0 0 0 0 0 0 0 0] [ 8 26 3019 1 0 17 0 3 59] [ 0 0 0 419 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 0 0 2 0 0 507 0 0 0] [ 2 0 34 0 0 3 707 0 0] [ 0 0 0 0 0 0 0 0 0] [ 4 33 187 0 0 0 0 2 2200]] Scoring Summary: (0) norm = 5.3631% (2) nneo = 3.6387% (3) infl = 0.0000% (5) dcis = 0.3929% (6) indc = 5.2279% (8) bckg = 9.3157% avg lbls = 2.9245% avg bckg = 9.3157% score = 3.5636% <** ================================================================= 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: [[ 606 10 591 10 0 36 21 1 66] [ 0 0 0 0 0 0 0 0 0] [ 288 12 1203 33 1 105 48 17 109] [ 6 0 74 218 0 10 6 5 1] [ 0 0 0 0 0 0 0 0 0] [ 14 1 190 7 0 60 15 2 7] [ 23 0 162 20 0 25 56 7 9] [ 0 0 0 0 0 0 0 0 0] [ 28 15 226 3 0 3 4 11 1045]] Scoring Summary: (0) norm = 54.8098% (2) nneo = 33.7555% (3) infl = 31.8750% (5) dcis = 79.7297% (6) indc = 81.4570% (8) bckg = 21.7228% avg lbls = 56.3254% avg bckg = 21.7228% score = 52.8652% <** ================================================================= 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: [[ 524 7 663 8 1 53 31 6 56] [ 0 0 0 0 0 0 0 0 0] [ 331 11 1457 44 0 165 65 24 116] [ 3 0 31 183 0 4 8 1 1] [ 0 0 0 0 0 0 0 0 0] [ 25 1 139 4 0 28 5 3 38] [ 19 2 268 28 0 72 55 15 5] [ 0 0 0 0 0 0 0 0 0] [ 33 28 172 0 0 2 8 3 936]] Scoring Summary: (0) norm = 61.1564% (2) nneo = 34.1618% (3) infl = 20.7792% (5) dcis = 88.4774% (6) indc = 88.1466% (8) bckg = 20.8122% avg lbls = 58.5443% avg bckg = 20.8122% score = 54.7711% <** ========================================================================