Abstract: Various example embodiments are described in which an anisotropic encoder-decoder convolutional neural network architecture is employed to process multiparametric magnetic resonance images for the generation of cancer predication maps. In some example embodiments, a simplified anisotropic encoder-decoder convolutional neural network architecture may include an encoder portion that is deeper than a decoder portion. In some example embodiments, simplified network architectures may be combined with test-time-augmentation in order to facilitate training and testing with a minimal number of test subjects.

Abstract: A computer system configured to generate an audio description of a media file is provided. The system includes a display, a memory, and a processor coupled to the display and the memory. The memory stores a media file, including video data that is accessible via a time index and audio data synchronized with the video data by the time index and a transcript of the audio data, including transcription data synchronized with the video data via the time index. The processor is configured to render, via the display, images from portions of the video data; render text from portions of the transcription data in synchrony with the images; receive input identifying a point within the time index; receive input specifying audio description data to associate with the point; store, in the memory, the audio description data; and store an association between the audio description data and the point.