You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: The last pooling layer (pool-5) activation maps of the ImageNet pre-trained AlexNet [4] (analogical to AlexNet-ImNet) and AlexNet-TL, obtained by processing two input images of Figure 2 (b,c), are shown in Figure 13 (a,b). The last pooling layer activation map summarizes the entire input image by highlighting which relative locations or neural reception fields relative to the image are activated. There are a total of 256 (6x6) reception fields in AlexNet [4]. Pooling units where the relative image location of the disease region is present in the image are highlighted with green boxes. Next, we reconstruct the original ILD images using the process of de-convolution, back-propagating with convolution and un-pooling from the activation maps of the chosen pooling units [72]. From the reconstructed images (Figure 13 bottom), we observe that with fine-tuning, AlexNet-TL detects and localizes objects of interest (ILD disease regions depicted in in Figure 2 (b) and (c)) better than AlexNet-ImNet. The filters shown in Figure 13 that better localize regions on the input images (Figure 2 (b) and (c)) respectively, produce relatively higher activations (in the top 5%) among all 512 reception field responses in the fine-tuned AlexNet-TL model. As observed in [73], the final CNN classification score can not be driven solely by a single strong activation in the receptions fields, but often by a sparse set of high activations (i.e., varying selective or sparse activations per input image).

Question: How is the original ILD images were reconstructed ?
A process consisting of deconvolution, back-propagation with convolution, and un-pooling from the activation maps of the pooling units was used to reconstruct the original ILD images.