================================================================= 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: [[1669 0 196 22 0 76 115 0 29] [ 0 0 0 0 0 0 0 0 0] [ 913 0 1133 151 0 629 230 0 77] [ 3 0 1 411 0 0 4 0 0] [ 0 0 0 0 0 0 0 0 0] [ 6 0 2 0 0 492 8 0 1] [ 0 0 2 26 0 14 701 0 3] [ 0 0 0 0 0 0 0 0 0] [ 109 0 48 8 0 12 55 0 2194]] Scoring Summary: (0) norm = 20.7879% (2) nneo = 63.8366% (3) infl = 1.9093% (5) dcis = 3.3399% (6) indc = 6.0322% (8) bckg = 9.5631% avg lbls = 19.1812% avg bckg = 9.5631% score = 18.2193% <** ================================================================= 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: [[ 831 0 181 50 0 28 165 0 86] [ 0 0 0 0 0 0 0 0 0] [ 550 0 492 152 0 300 172 0 150] [ 11 0 5 273 0 15 16 0 0] [ 0 0 0 0 0 0 0 0 0] [ 30 0 75 15 0 133 40 0 3] [ 34 0 35 47 0 61 125 0 0] [ 0 0 0 0 0 0 0 0 0] [ 133 0 66 6 0 5 44 0 1081]] Scoring Summary: (0) norm = 38.0313% (2) nneo = 72.9075% (3) infl = 14.6875% (5) dcis = 55.0676% (6) indc = 58.6093% (8) bckg = 19.0262% avg lbls = 47.8606% avg bckg = 19.0262% score = 44.9772% <** ================================================================= 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: [[814 0 209 27 0 57 188 0 54] [ 0 0 0 0 0 0 0 0 0] [657 0 593 122 0 423 351 0 67] [ 5 0 1 210 0 4 11 0 0] [ 0 0 0 0 0 0 0 0 0] [ 61 0 50 8 0 52 64 0 8] [ 33 0 23 51 0 217 139 0 1] [ 0 0 0 0 0 0 0 0 0] [105 0 38 2 0 4 35 0 998]] Scoring Summary: (0) norm = 39.6590% (2) nneo = 73.2038% (3) infl = 9.0909% (5) dcis = 78.6008% (6) indc = 70.0431% (8) bckg = 15.5668% avg lbls = 54.1195% avg bckg = 15.5668% score = 50.2643% <** ======================================================================== ================================================================= hyp_vit_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[2082 0 18 0 0 0 0 0 7] [ 0 0 0 0 0 0 0 0 0] [ 13 0 3099 1 0 2 6 0 12] [ 0 0 0 419 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 509 0 0 0] [ 0 0 0 0 0 0 746 0 0] [ 0 0 0 0 0 0 0 0 0] [ 1 0 1 1 0 0 0 0 2423]] Scoring Summary: (0) norm = 1.1865% (2) nneo = 1.0852% (3) infl = 0.0000% (5) dcis = 0.0000% (6) indc = 0.0000% (8) bckg = 0.1237% avg lbls = 0.4543% avg bckg = 0.1237% score = 0.4213% <** ================================================================= hyp_vit_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 940 0 280 7 0 9 17 0 88] [ 0 0 0 0 0 0 0 0 0] [ 303 0 1233 31 0 110 58 0 81] [ 5 0 35 265 0 2 12 0 1] [ 0 0 0 0 0 0 0 0 0] [ 4 0 127 5 0 147 9 0 4] [ 9 0 77 21 0 15 176 0 4] [ 0 0 0 0 0 0 0 0 0] [ 36 0 61 5 0 1 7 0 1225]] Scoring Summary: (0) norm = 29.9031% (2) nneo = 32.1035% (3) infl = 17.1875% (5) dcis = 50.3378% (6) indc = 41.7219% (8) bckg = 8.2397% avg lbls = 34.2508% avg bckg = 8.2397% score = 31.6496% <** ================================================================= hyp_vit_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 903 0 365 0 0 6 11 0 64] [ 0 0 0 0 0 0 0 0 0] [ 392 0 1577 38 0 67 67 0 72] [ 2 0 11 208 0 0 10 0 0] [ 0 0 0 0 0 0 0 0 0] [ 5 0 126 1 0 73 16 0 22] [ 7 0 200 19 0 21 214 0 3] [ 0 0 0 0 0 0 0 0 0] [ 13 0 44 0 0 3 10 0 1112]] Scoring Summary: (0) norm = 33.0615% (2) nneo = 28.7393% (3) infl = 9.9567% (5) dcis = 69.9588% (6) indc = 53.8793% (8) bckg = 5.9222% avg lbls = 39.1191% avg bckg = 5.9222% score = 35.7994% <** ========================================================================