Finalize tutorial

Author: mldiego

code/nnv/engine/nn/layers/ConcatenationLayer.m

@@ -128,6 +128,7 @@
 
             % Create output set
             outputs = ImageStar(new_V, inputs{indexMax}.C, inputs{indexMax}.d, inputs{indexMax}.pred_lb, inputs{indexMax}.pred_ub);
+            
         end
 
         % handle multiple inputs

code/nnv/examples/Tutorial/SPIE/Segmentation/data/slice_ISBI_1_1_75.mat

@@ -0,0 +1,9 @@
+% verify_bias_field;
+% 
+% clc;clear;
+
+verify_gamma_correction;
+
+clc;clear;
+
+verify_random_noise;

code/nnv/examples/Tutorial/SPIE/Segmentation/models/model64.onnx

@@ -0,0 +1,17 @@
+function verOut = output_vs_mask(img, mask)
+
+    diff_pixels = find(img ~= mask);
+
+    verOut = img;
+
+    for i=1:length(diff_pixels)
+        if img(diff_pixels(i)) == 2
+            continue
+        elseif img(diff_pixels(i)) == 1
+            verOut(diff_pixels(i)) = -1;
+        else
+            verOut(diff_pixels(i)) = -2;
+        end
+    end
+
+end

code/nnv/examples/Tutorial/SPIE/Segmentation/results/biasfield_output.mat

@@ -0,0 +1,24 @@
+function ver_img = verify_output(imageSet)
+
+    % Get data ranges
+    [lb,ub] = imageSet.estimateRanges;
+    
+    % 1) get correctly classified as 0 (background)
+    ver_background = (ub <= 0);
+    ver_background = ~ver_background;
+    
+    % 2) get correctly classified as 1 (lession)
+    ver_lesion = (lb > 0);
+
+    % 3) get verified output
+    ver_img = 2*ones(size(lb)); % pixel = 2 -> unknown
+    
+    background = find(ver_background == 0); % 0
+    ver_img(background) = 0;
+    
+    lesion = find(ver_lesion == 1); % 1
+    ver_img(lesion) = 1;
+
+
+end
+

code/nnv/examples/Tutorial/SPIE/Segmentation/results/gama_output.mat

@@ -3,15 +3,120 @@
 % Load one slice from ISBI
 load("data/slice_ISBI_1_1_75.mat");
 
-% Create input set
+% Load Unet
+onnx = importNetworkFromONNX("models/model64.onnx");
+% analyzeNetwork(onnx)
+net = matlab2nnv(onnx);
+windowSize = 64; % from trained model
+
+% Bias Field
+order = 3;
+coeff = 0.01;
+img_bf = BiasField(flair, order, coeff);
+
+% Crop background before patches (for all data)
+flair = flair(31:158, 31:158);
+img_bf = img_bf(31:158, 31:158);
+mask = mask(31:158, 31:158);
+
+% From here, to verify slice, we need to verify 4 patches
+
+% Define verification parameters (reach method)
+reachOptions.reachMethod = "relax-star-area-reduceMem";
+reachOptions.relaxFactor = 1;
+reachOptions.lp_solver = "gurobi"; % (optional)
+
+% Create bounds of input set
 lb = flair;
 ub = img_bf;
 
-windowSize = 64;
 
-% Crop background before patches
-flair = flair(35:158, 30:153);
 
-% From here, to verify slice, we need to verify 4 patches
+% Patch 1: Left-top corner
+InputSet1 = ImageStar(lb(1:64,1:64), ub(1:64,1:64));
+
+% Compute reachability
+tic;
+OutputSet1 = net.reach(InputSet1, reachOptions);
+toc;
+
+% Verified output
+verOut1 = verify_output(OutputSet1);
+
+
+
+% Patch 2: Right-top corner
+InputSet2 = ImageStar(lb(1:64,65:128), ub(1:64,65:128));
+
+% Compute reachability
+tic;
+OutputSet2 = net.reach(InputSet2, reachOptions);
+toc;
+
+% Verified output
+verOut2 = verify_output(OutputSet2);
+
+
+
+% Patch 3: Right-bottom corner
+InputSet3 = ImageStar(lb(65:128,65:128), ub(65:128,65:128));
+
+% Compute reachability
+tic;
+OutputSet3 = net.reach(InputSet3, reachOptions);
+toc;
+
+% Verified output
+verOut3 = verify_output(OutputSet3);
+
+
+
+% Patch 4: Lft-bottom corner
+InputSet4 = ImageStar(lb(65:128,1:64), ub(65:128,1:64));
+
+% Compute reachability
+tic;
+OutputSet4 = net.reach(InputSet4, reachOptions);
+toc;
+
+% Verified reachability
+verOut4 = verify_output(OutputSet4);
+
+% Output reachability
+outputSlice = [verOut1 verOut2; verOut3 verOut4];
+save("results/biasfield_output.mat","outputSlice")
+
+imshow(outputSlice, [0,2], colormap=hsv(3))
+colorbar('XTickLabel', {'Background', 'Lesion', 'Unknown'}, 'XTick',[0,1,2])
+
+% Verified output
+verifiedSlice = output_vs_mask(outputSlice, mask);
+imshow(verifiedSlice, [-2,2], colormap=hsv(5))
+colorbar('XTickLabel', {'False Negative', 'False Positive', 'Background', 'Lesion', 'Unknown'}, 'XTick',[-2,-1,0,1,2])
+
+% Verified lesion
+overlay = labeloverlay(flair,verifiedSlice,'transparency',0.3);
+imshow(overlay,[-2 2]);
+
+figure;
+subplot(1,4,1);
+mi_f = min(flair, [], 'all');
+ma_f = max(flair,[], 'all');
+overlay = labeloverlay(flair,mask,'transparency',0.3);
+imshow(overlay,[mi_f, ma_f]);
+title("Label mask")
+
+subplot(1,4,2);
+overlay = labeloverlay(flair,verifiedSlice==1,'transparency',0.3);
+imshow(overlay,[mi_f, ma_f]);
+title('Verified lession')
+
+subplot(1,4,3);
+overlay = labeloverlay(flair,verifiedSlice==2,'transparency',0.3);
+imshow(overlay,[mi_f, ma_f]);
+title("Unknown")
 
-% Left-top corner
+subplot(1,4,4);
+overlay = labeloverlay(flair,verifiedSlice==-1,'transparency',0.3);
+imshow(overlay,[mi_f, ma_f]);
+title("False positives")