Patient Re-Identification Based on Deep Metric Learning in Trunk Computed Tomography Images Acquired from Devices from Different Vendors.

During radiologic interpretation, radiologists read patient identifiers from the metadata of medical images to recognize the patient being examined. However, it is challenging for radiologists to identify "incorrect" metadata and patient identification errors. We propose a method that uses a patient re-identification technique to link correct metadata to an image set of computed tomography images of a trunk with lost or wrongly assigned metadata. This method is based on a feature vector matching technique that uses a deep feature extractor to adapt to the cross-vendor domain contained in the scout computed tomography image dataset. To identify "incorrect" metadata, we calculated the highest similarity score between a follow-up image and a stored baseline image linked to the correct metadata. The re-identification performance tests whether the image with the highest similarity score belongs to the same patient, i.e., whether the metadata attached to the image are correct. The similarity scores between the follow-up and baseline images for the same "correct" patients were generally greater than those for "incorrect" patients. The proposed feature extractor was sufficiently robust to extract individual distinguishable features without additional training, even for unknown scout computed tomography images. Furthermore, the proposed augmentation technique further improved the re-identification performance of the subset for different vendors by incorporating changes in width magnification due to changes in patient table height during each examination. We believe that metadata checking using the proposed method would help detect the metadata with an "incorrect" patient identifier assigned due to unavoidable errors such as human error.

Unsupervised learning as a computational principle works in visual learning of natural scenes, but not of artificial stimuli.

The question of whether we learn exposed visual features remains a subject of controversy. A prevalent computational model suggests that visual features frequently exposed to observers in natural environments are likely to be learned. However, this unsupervised learning model appears to be contradicted by the significant body of experimental results with human participants that indicates visual perceptual learning (VPL) of visible task-irrelevant features does not occur with frequent exposure. Here, we demonstrate a resolution to this controversy with a new finding: Exposure to a dominant global orientation as task-irrelevant leads to VPL of the orientation, particularly when the orientation is derived from natural scene images, whereas VPL did not occur with artificial images even with matched distributions of local orientations and spatial frequencies to natural scene images. Further investigation revealed that this disparity arises from the presence of higher-order statistics derived from natural scene images-global structures such as correlations between different local orientation and spatial frequency channels. Moreover, behavioral and neuroimaging results indicate that the dominant orientation from these higher-order statistics undergoes less attentional suppression than that from artificial images, which may facilitate VPL. Our results contribute to resolving the controversy by affirming the validity of unsupervised learning models for natural scenes but not for artificial stimuli. They challenge the assumption that VPL occurring in everyday life can be predicted by laws governing VPL for conventionally used artificial stimuli.

Sequential post-translational modifications regulate damaged DNA-binding protein DDB2 function.

Nucleotide excision repair (NER) is a major DNA repair system and hereditary defects in this system cause critical genetic diseases (e.g. xeroderma pigmentosum, Cockayne syndrome and trichothiodystrophy). Various proteins are involved in the eukaryotic NER system and undergo several post-translational modifications. Damaged DNA-binding protein 2 (DDB2) is a DNA damage recognition factor in the NER pathway. We previously demonstrated that DDB2 was SUMOylated in response to UV irradiation; however, its physiological roles remain unclear. We herein analysed several mutants and showed that the N-terminal tail of DDB2 was the target for SUMOylation; however, this region did not contain a consensus SUMOylation sequence. We found a SUMO-interacting motif (SIM) in the N-terminal tail that facilitated SUMOylation. The ubiquitination of a SUMOylation-deficient DDB2 SIM mutant was decreased, and its retention of chromatin was prolonged. The SIM mutant showed impaired NER, possibly due to a decline in the timely handover of the lesion site to XP complementation group C. These results suggest that the SUMOylation of DDB2 facilitates NER through enhancements in ubiquitination.