An image processing method for automatic detection of biological structures in a multi-channel image obtained from a biological tissue sample being stained by multiple stains, the method comprising: - unmixing the multi-channel image to provide an unmixed image per channel , each channel being representative of one of the biological structures, - detecting of candidate locations for the biological structures in the unmixed images by applying an image processing algorithm, - for each of the candidate locations, extracting a stack of image patches (528.1, 528.2, 528.3, 528.4, 528.5) having a predefined size (NxN; axb) from the unmixed images, the image patches of the stack comprising the candidate location, each one of the stacks of image patches comprising one image patch per channel, - sequentially entering the stacks of image patches into a trained convolutional neural network , the convolutional neural network comprising at least convolutional layers and sub-sampling layers in alternating order, the first one (C1) of the convolutional layers being coupled to inputs of the convolutional neural network, each one of the inputs being assigned to one of the channels , the first one (C1) of the convolutional layers having a number of feature maps (m1, m2, m3), the convolutional neural network being configured for connection mapping of the inputs to the feature maps of the first one of the convolutional layers using co-location data being descriptive of groups of the stains, each group comprising co-located stains, in order to map sub-sets of the channels that are representative of co-located biological features to a common feature map, - outputting a probability map representing a probability for the presence of the biological features in the multi-channel image from an output of the convolutional neural network. - unmixing the multi-channel image to provide an unmixed image per channel , each channel being representative of one of the biological structures, - detecting of candidate locations for the biological structures in the unmixed images by applying an image processing algorithm, - for each of the candidate locations, extracting a stack of image patches (528.1, 528.2, 528.3, 528.4, 528.5) having a predefined size (NxN; axb) from the unmixed images, the image patches of the stack comprising the candidate location, each one of the stacks of image patches comprising one image patch per channel, - sequentially entering the stacks of image patches into a trained convolutional neural network , the convolutional neural network comprising at least convolutional layers and sub-sampling layers in alternating order, the first one (C1) of the convolutional layers being coupled to inputs of the convolutional neural network, each one of the inputs being assigned to one of the channels , the first one (C1) of the convolutional layers having a number of feature maps (m1, m2, m3), the convolutional neural network being configured for connection mapping of the inputs to the feature maps of the first one of the convolutional layers using co-location data being descriptive of groups of the stains, each group comprising co-located stains, in order to map sub-sets of the channels that are representative of co-located biological features to a common feature map, - outputting a probability map representing a probability for the presence of the biological features in the multi-channel image from an output of the convolutional neural network. <EOS>