================================================================= hyp_drt_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[1595 0 91 57 0 88 213 0 63] [ 0 0 0 0 0 0 0 0 0] [ 515 0 1223 189 0 542 525 0 139] [ 0 0 0 418 0 1 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 509 0 0 0] [ 1 0 2 18 0 25 700 0 0] [ 0 0 0 0 0 0 0 0 0] [ 59 0 24 7 0 15 68 0 2253]] Scoring Summary: (0) norm = 24.3000% (2) nneo = 60.9639% (3) infl = 0.2387% (5) dcis = 0.0000% (6) indc = 6.1662% (8) bckg = 7.1311% avg lbls = 18.3338% avg bckg = 7.1311% score = 17.2135% <** ================================================================= hyp_drt_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 763 0 150 52 0 53 214 0 109] [ 0 0 0 0 0 0 0 0 0] [ 474 0 470 106 0 380 255 0 131] [ 11 0 14 215 0 39 40 0 1] [ 0 0 0 0 0 0 0 0 0] [ 17 0 61 20 0 142 52 0 4] [ 20 0 34 31 0 46 170 0 1] [ 0 0 0 0 0 0 0 0 0] [ 93 0 21 8 0 5 53 0 1155]] Scoring Summary: (0) norm = 43.1022% (2) nneo = 74.1189% (3) infl = 32.8125% (5) dcis = 52.0270% (6) indc = 43.7086% (8) bckg = 13.4831% avg lbls = 49.1538% avg bckg = 13.4831% score = 45.5868% <** ================================================================= hyp_drt_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 677 0 168 63 0 79 286 0 76] [ 0 0 0 0 0 0 0 0 0] [ 540 0 501 150 0 409 509 0 104] [ 9 0 4 176 0 18 24 0 0] [ 0 0 0 0 0 0 0 0 0] [ 46 0 39 9 0 65 74 0 10] [ 20 0 23 66 0 152 201 0 2] [ 0 0 0 0 0 0 0 0 0] [ 70 0 17 3 0 3 39 0 1050]] Scoring Summary: (0) norm = 49.8147% (2) nneo = 77.3610% (3) infl = 23.8095% (5) dcis = 73.2510% (6) indc = 56.6810% (8) bckg = 11.1675% avg lbls = 56.1835% avg bckg = 11.1675% score = 51.6819% <** ======================================================================== ================================================================= 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: [[1719 0 221 10 0 15 65 0 77] [ 0 0 0 0 0 0 0 0 0] [ 569 0 1548 143 0 480 240 0 153] [ 0 0 0 417 0 0 2 0 0] [ 0 0 0 0 0 0 0 0 0] [ 5 0 1 0 0 490 7 0 6] [ 5 0 9 10 0 29 689 0 4] [ 0 0 0 0 0 0 0 0 0] [ 30 0 18 4 0 5 13 0 2356]] Scoring Summary: (0) norm = 18.4148% (2) nneo = 50.5905% (3) infl = 0.4773% (5) dcis = 3.7328% (6) indc = 7.6408% (8) bckg = 2.8854% avg lbls = 16.1712% avg bckg = 2.8854% score = 14.8427% <** ================================================================= 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: [[1014 0 119 25 0 18 50 0 115] [ 0 0 0 0 0 0 0 0 0] [ 489 0 718 75 0 260 135 0 139] [ 6 0 10 289 0 1 13 0 1] [ 0 0 0 0 0 0 0 0 0] [ 4 0 52 6 0 200 26 0 8] [ 9 0 33 15 0 27 212 0 6] [ 0 0 0 0 0 0 0 0 0] [ 38 0 18 5 0 0 17 0 1257]] Scoring Summary: (0) norm = 24.3848% (2) nneo = 60.4626% (3) infl = 9.6875% (5) dcis = 32.4324% (6) indc = 29.8013% (8) bckg = 5.8427% avg lbls = 31.3537% avg bckg = 5.8427% score = 28.8026% <** ================================================================= 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: [[ 964 0 188 23 0 21 60 0 93] [ 0 0 0 0 0 0 0 0 0] [ 608 0 928 108 0 275 174 0 120] [ 3 0 3 212 0 3 10 0 0] [ 0 0 0 0 0 0 0 0 0] [ 6 0 55 4 0 126 19 0 33] [ 9 0 101 35 0 85 232 0 2] [ 0 0 0 0 0 0 0 0 0] [ 29 0 13 1 0 3 17 0 1119]] Scoring Summary: (0) norm = 28.5397% (2) nneo = 58.0660% (3) infl = 8.2251% (5) dcis = 48.1481% (6) indc = 50.0000% (8) bckg = 5.3299% avg lbls = 38.5958% avg bckg = 5.3299% score = 35.2692% <** ========================================================================