A computer implemented convolutional neural network -based learning method for a CNN to perform image segmentation to an image provided at its input to produce a labeled image as an output by using a learning device, the CNN including a number of N convolutional layers (100_1, 100_2, 100_3,...,100_N) capable of applying one or more convolution operations to an input image or its corresponding one or more encoded input feature maps to thereby sequentially generate one or more encoded output feature maps, the method comprising steps of: (a) the learning device, if the input image is acquired, performing a process of randomly selecting a unit of the CNN to be dropped out among the units; (b) the learning device (i) feeding the input image into at least one transformed CNN in which the specific unit is dropped out, to thereby generate adjusted encoded output feature maps and (ii) generating a CNN output by using the adjusted encoded output feature maps; and (c) the learning device calculating one or more losses by using the CNN output and its corresponding ground truth and adjusting at least one parameter of the transformed CNN by backpropagating the losses, characterized in that (i) each of the N convolutional layers of the CNN has L sub-convolutional layers (100_3_A, 100_3_B, 100_3_C; 100_4_A, 100_4_B, 100_4_C) and M sub-residual networks (110_3_A, 110_3_B, 110_3_C; 110_4_A, 110_4_B, 110_4_C) having each different bypassing route for bypassing at least one of the L sub-convolutional layers, (ii) the CNN further includes a number of N deconvolutional layers (200_1, 200_2, 200_3,...,200_N) capable of applying one or more deconvolutional operations to at least part of the adjusted encoded output feature maps or its corresponding adjusted decoded input feature maps to thereby sequentially generate one or more adjusted decoded output feature maps and each of the N deconvolutional layers includes L sub-deconvolutional layers, each of the N deconvolutional layers includes M sub-residual networks having each different bypassing route through which at least one of the L sub-deconvolutional layers is bypassed, wherein the NxM sub-residual networks include residual networks capable of feed-forwarding the adjusted decoded input feature maps to its corresponding next deconvolutional layer through bypassing at least one of the plurality of deconvolutional layers, (iii) the CNN further includes a plurality of K intermediate layers (300_1, 300_2, 300_3,...,300_N), which are located between at least one of the convolutional layers and at least one of the deconvolutional layers, capable of applying one or more convolution operations to one or more inputs fed thereto and then feeding one or more outputs therefrom into at least one of the deconvolutional layers, and in that, at the step of (a), the randomly selected unit to be dropped out among the units is at least one specific intermediate layer and at least one specific sub-residual network among the K intermediate layers and the 2xNxM sub-residual networks, respectively. (a) the learning device, if the input image is acquired, performing a process of randomly selecting a unit of the CNN to be dropped out among the units; (b) the learning device (i) feeding the input image into at least one transformed CNN in which the specific unit is dropped out, to thereby generate adjusted encoded output feature maps and (ii) generating a CNN output by using the adjusted encoded output feature maps; and (i) feeding the input image into at least one transformed CNN in which the specific unit is dropped out, to thereby generate adjusted encoded output feature maps and (ii) generating a CNN output by using the adjusted encoded output feature maps; and (c) the learning device calculating one or more losses by using the CNN output and its corresponding ground truth and adjusting at least one parameter of the transformed CNN by backpropagating the losses, (i) each of the N convolutional layers of the CNN has L sub-convolutional layers (100_3_A, 100_3_B, 100_3_C; 100_4_A, 100_4_B, 100_4_C) and M sub-residual networks (110_3_A, 110_3_B, 110_3_C; 110_4_A, 110_4_B, 110_4_C) having each different bypassing route for bypassing at least one of the L sub-convolutional layers, (ii) the CNN further includes a number of N deconvolutional layers (200_1, 200_2, 200_3,...,200_N) capable of applying one or more deconvolutional operations to at least part of the adjusted encoded output feature maps or its corresponding adjusted decoded input feature maps to thereby sequentially generate one or more adjusted decoded output feature maps and each of the N deconvolutional layers includes L sub-deconvolutional layers, each of the N deconvolutional layers includes M sub-residual networks having each different bypassing route through which at least one of the L sub-deconvolutional layers is bypassed, wherein the NxM sub-residual networks include residual networks capable of feed-forwarding the adjusted decoded input feature maps to its corresponding next deconvolutional layer through bypassing at least one of the plurality of deconvolutional layers, (iii) the CNN further includes a plurality of K intermediate layers (300_1, 300_2, 300_3,...,300_N), which are located between at least one of the convolutional layers and at least one of the deconvolutional layers, capable of applying one or more convolution operations to one or more inputs fed thereto and then feeding one or more outputs therefrom into at least one of the deconvolutional layers,