A method of classifying an image, comprising: applying a computer vision procedure to the image to detect therein candidate image regions suspected as being occupied by a target; presenting to an observer each candidate image region as a visual stimulus, while collecting neurophysiological signals from a brain of said observer; the method being characterized in: simultaneously processing said presented candidate image region and said neurophysiological signals at least by applying a multimodal convolutional neural network classifier to said displayed candidate image region and said neurophysiological signals, to identify in said neurophysiological signals a neurophysiological event indicative of a detection of the target by said observer, and to calculate a score describing a correlation between detection by said multimodal CNN and a detection by said observer, wherein said multimodal CNN comprises a first convolutional neural subnetwork for processing said neurophysiological signals, a second CNS for processing the candidate image region, and a shared subnetwork having a neural network layer for receiving and combining outputs from both said first CNS and said second CNS; and determining an existence of said target in the image is based, at least in part, on said score. applying a computer vision procedure to the image to detect therein candidate image regions suspected as being occupied by a target; presenting to an observer each candidate image region as a visual stimulus, while collecting neurophysiological signals from a brain of said observer; the method being characterized in: simultaneously processing said presented candidate image region and said neurophysiological signals at least by applying a multimodal convolutional neural network classifier to said displayed candidate image region and said neurophysiological signals, to identify in said neurophysiological signals a neurophysiological event indicative of a detection of the target by said observer, and to calculate a score describing a correlation between detection by said multimodal CNN and a detection by said observer, wherein said multimodal CNN comprises a first convolutional neural subnetwork for processing said neurophysiological signals, a second CNS for processing the candidate image region, and a shared subnetwork having a neural network layer for receiving and combining outputs from both said first CNS and said second CNS; and determining an existence of said target in the image is based, at least in part, on said score. simultaneously processing said presented candidate image region and said neurophysiological signals at least by applying a multimodal convolutional neural network classifier to said displayed candidate image region and said neurophysiological signals, to identify in said neurophysiological signals a neurophysiological event indicative of a detection of the target by said observer, and to calculate a score describing a correlation between detection by said multimodal CNN and a detection by said observer, wherein said multimodal CNN comprises a first convolutional neural subnetwork for processing said neurophysiological signals, a second CNS for processing the candidate image region, and a shared subnetwork having a neural network layer for receiving and combining outputs from both said first CNS and said second CNS; and determining an existence of said target in the image is based, at least in part, on said score.