Embodiments may provide methods by which fusion of information may be applied to uncertainty reduction in the results of classifier-based data analysis. For example, a method for data analysis may comprise generating a plurality of sets of data samples, each set of data samples representing a portion of input data at plurality of scales, each data sample in a set may represent the portion of the input data at a different scale and location, generating a feature map from each data sample of at least one set of data samples by learning and aggregating features using a first multi-layer convolutional processing, each data sample may be processed with multi-layer convolutional processing separately from other data samples, and generating a feature map by combining the feature maps from the data samples of each set of data samples by performing multiple-scale-multiple-location label fusion using a second multi-layer convolutional processing.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for data analysis comprising: receiving input data; generating a plurality of sets of data samples, each set of data samples representing a portion of the received input data at plurality of scales, wherein each data sample in a set represents the portion of the received input data at a different scale; generating a feature map from each data sample of at least one set of data samples by learning and aggregating features using a first multi-layer convolutional processing, wherein each data sample is processed with multi-layer convolutional processing separately from other data samples; and generating a feature map for the at least one set of data samples by combining the feature maps from the data samples of each set of data samples by performing multiple-scale-multiple-location fusion using a second multi-layer convolutional processing. 2. The method of claim 1, wherein the data samples in each set of data samples are overlapping data samples. 3. The method of claim 1, wherein the data samples in each set of data samples are non-overlapping data samples. 4. The method of claim 1, wherein each layer of convolutional processing of the first multi-layer convolutional processing comprises at least one type of processing selected from a set comprising convolutional layer processing, pooling layer processing, Rectified Linear Units layer processing, dropout layer processing, and loss layer processing. 5. The method of claim 4, wherein each layer of convolutional processing of the second multi-layer convolutional processing comprises at least one type of processing selected from a set comprising convolutional layer processing, pooling layer processing, Rectified Linear Units layer processing, dropout layer processing, and loss layer processing, and wherein the second multi-layer convolutional processing and the second multi-layer convolutional processing comprises different layers of convolutional processing. 6. The method of claim 5, wherein the input data comprises image data. 7. A computer program product for data analysis, the computer program product comprising a non-transitory computer readable storage having program instructions embodied therewith, the program instructions executable by a computer, to cause the computer to perform a method comprising: receiving input data; generating a plurality of sets of data samples, each set of data samples representing a portion of the received input data at plurality of scales, wherein each data sample in a set represents the portion of the received input data at a different scale; generating a feature map from each data sample of at least one set of data samples by learning and aggregating features using a first multi-layer convolutional processing, wherein each data sample is processed with multi-layer convolutional processing separately from other data samples; and generating a feature map for the at least one set of data samples by combining the feature maps from the data samples of each set of data samples by performing multiple-scale-multiple-location label fusion using a second multi-layer convolutional processing. 8. The computer program product of claim 7, wherein the data samples in each set of data samples are overlapping data samples. 9. The computer program product of claim 7, wherein the data samples in each set of data samples are non-overlapping data samples. 10. The computer program product of claim 7, wherein each layer of convolutional processing of the first multi-layer convolutional processing comprises at least one type of processing selected from a set comprising convolutional layer processing, pooling layer processing, Rectified Linear Units layer processing, dropout layer processing, and loss layer processing. 11. The computer program product of claim 10, wherein each layer of convolutional processing of the second multi-layer convolutional processing comprises at least one type of processing selected from a set comprising convolutional layer processing, pooling layer processing, Rectified Linear Units layer processing, dropout layer processing, and loss layer processing, and wherein the second multi-layer convolutional processing and the second multi-layer convolutional processing comprises different layers of convolutional processing. 12. The computer program product of claim 11, wherein the input data comprises image data. 13. A system for predicting metastasis of a cancer, the system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor to perform: receiving input data; generating a plurality of sets of data samples, each set of data samples representing a portion of the received input data at plurality of scales, wherein each data sample in a set represents the portion of the received input data at a different scale; generating a feature map from each data sample of at least one set of data samples by learning and aggregating features using a first multi-layer convolutional processing, wherein each data sample is processed with multi-layer convolutional processing separately from other data samples; and generating a feature map for the at least one set of data samples by combining the feature maps from the data samples of each set of data samples by performing multiple-scale-multiple-location label fusion using a second multi-layer convolutional processing. 14. The system of claim 13, wherein the data samples in each set of data samples are overlapping data samples. 15. The system of claim 13, wherein the data samples in each set of data samples are non-overlapping data samples. 16. The system of claim 13, wherein each layer of convolutional processing of the first multi-layer convolutional processing comprises at least one type of processing selected from a set comprising convolutional layer processing, pooling layer processing, Rectified Linear Units layer processing, dropout layer processing, and loss layer processing. 17. The system of claim 16, wherein each layer of convolutional processing of the second multi-layer convolutional processing comprises at least one type of processing selected from a set comprising convolutional layer processing, pooling layer processing, Rectified Linear Units layer processing, dropout layer processing, and loss layer processing, and wherein the second multi-layer convolutional processing and the second multi-layer convolutional processing comprises different layers of convolutional processing. 18. The system of claim 17, wherein the input data comprises image data.