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Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture, or temporal information to detect polyps, so they have limited sensitivity and specificity. This study proposes a method to extract possible polyp features automatically using convolutional neural networks (CNNs). The objective of this work aims at building up a light-weight dual encoder-decoder model structure for polyp detection in colonoscopy Images. This proposed model, though with a relatively shallow structure, is expected to have the capability of a similar performance to the methods with much deeper structures. The proposed CAD model consists of two sequential encoder-decoder networks that consist of several CNN layers and full connection layers. The front end of the model is a hetero-associator (also known as hetero-encoder) that uses backpropagation learning to generate a set of reliably corrupted labeled images with a certain degree of similarity to a ground truth image, which eliminates the need for a large amount of training data that is usually required for medical images tasks. This dual CNN architecture generates a set of noisy images that are similar to the labeled data to train its counterpart, the auto-associator (also known as auto-encoder), in order to increase the successor's discriminative power in classification. The auto-encoder is also equipped with CNNs to simultaneously capture the features of the labeled images that contain noise. The proposed method uses features that are learned from open medical datasets and the dataset of Zhejiang University (ZJU), which contains around one thousand images. The performance of the proposed architecture is compared with a state-of-the-art detection model in terms of the metrics of the Jaccard index, the DICE similarity score, and two other geometric measures. The improvements in the performance of the proposed model are attributed to the effective reduction in false positives in the auto-encoder and the generation of noisy candidate images by the hetero-encoder., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Liver transplantation is the ultimate treatment for severe hepatic failure to date. However, the limited supply of donor organs has severely hampered this treatment. So far, great potentials of using mesenchymal stem cells (MSCs) to replenish the hepatic cell population have been shown; nevertheless, there still is a lack of an optimal three-dimensional scaffold for generation of well-transplantable hepatic tissues. In this study, we utilized a cryo-chemical decellularization method which combines physical and chemical approach to generate acellular liver scaffolds (ALS) from the whole liver. The produced ALS provides a biomimetic three-dimensional environment to support hepatic differentiation of MSCs, evidenced by expression of hepatic-associated genes and marker protein, glycogen storage, albumin secretion, and urea production. It is also found that hepatic differentiation of MSCs within the ALS is much more efficient than two-dimensional culture in vitro. Importantly, the hepatic-like tissues (HLT) generated by repopulating ALS with MSCs are able to act as functional grafts and rescue lethal hepatic failure after transplantation in vivo. In summary, the cryo-chemical method used in this study is suitable for decellularization of liver and create acellular scaffolds that can support hepatic differentiation of MSCs and be used to fabricate functional tissue-engineered liver constructs., (Copyright © 2014 Elsevier Ltd. All rights reserved.)