Ruptured liver abscess presenting as pneumoperitoneum caused by Klebsiella pneumoniae: a case report.

BACKGROUND: Spontaneous gas-forming pyogenic liver abscess (GFPLA) is a rare complication with a high fatality rate in spite of aggressive management. Clinical spectrum of GFPLA can mimic hollow viscus perforation as it usually accompanied by pneumoperitoneum and peritonitis. Up to now, GFPLA has not been well studied in Vietnam. CASE PRESENTATION: We reported here a case with pneumoperitoneum caused by ruptured liver abscess in a 41-year-old man with a history of treated duodenal ulcer and uncontrolled type II diabetes mellitus. He had an epigastric pain associated with a high fever. Patient was diagnosed peritonitis and pneumoperitoneum presumed to be secondary to perforation of a hollow viscus and subjected to emergency laparotomy. We did not find any gastrointestinal perforation. Surprisingly, we detected a 4 cm × 4 cm pus-containing abscess in the left liver lobe of the liver. The abscess was ruptured. Pus was running into abdominal cavity through one hole. The abscess and abdominal cavities were cleaned up and abscess and abdominal drainages were performed. K. pneumoniae was isolated from culture of the abscess. The histopathological examination of the abscess did not yield any evidence of malignancy. Blood glucose levels were controlled. Antibiotic therapy was used according to antibiogram. A reassessment chest X-ray showed no air-fluid level or subdiaphragmatic air by the hospital day 14. Patient eventually made a full recovery and was discharged home 23 days after the operation. CONCLUSIONS: Ruptured GFPLA is a life-threatening complication. It is usually accompanied by peritonitis and pneumoperitoneum and can imitate hollow viscous perforation. In these cases, CT scan should be performed whenever it is possible to make a correct diagnosis. When the abscess has small size, partial hepatectomy might not be necessary and could be replaced by a careful cleaning and drainage of the abscess. Patient could show a good postoperative recovery following an appropriate antibiotic therapy.

Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks.

Anomaly detection is important in many real-life applications. Recently, self-supervised learning has greatly helped deep anomaly detection by recognizing several geometric transformations. However these methods lack finer features, usually highly depend on the anomaly type, and do not perform well on fine-grained problems. To address these issues, we first introduce in this work three novel and efficient discriminative and generative tasks which have complementary strength: (i) a piece-wise jigsaw puzzle task focuses on structure cues; (ii) a tint rotation recognition is used within each piece, taking into account the colorimetry information; (iii) and a partial re-colorization task considers the image texture. In order to make the re-colorization task more object-oriented than background-oriented, we propose to include the contextual color information of the image border via an attention mechanism.We then present a new out-of-distribution detection function and highlight its better stability compared to existing methods. Along with it, we also experiment different score fusion functions. Finally, we evaluate our method on an extensive protocol composed of various anomaly types, from object anomalies, style anomalies with fine-grained classification to local anomalies with face anti-spoofing datasets. Our model significantly outperforms state-of-the-art with up to 36% relative error improvement on object anomalies and 40% on face anti-spoofing problems.

Enhanced patterns of oriented edge magnitudes for face recognition and image matching.

A good feature descriptor is desired to be discriminative, robust, and computationally inexpensive in both terms of time and storage requirement. In the domain of face recognition, these properties allow the system to quickly deliver high recognition results to the end user. Motivated by the recent feature descriptor called Patterns of Oriented Edge Magnitudes (POEM), which balances the three concerns, this paper aims at enhancing its performance with respect to all these criteria. To this end, we first optimize the parameters of POEM and then apply the whitened principal-component-analysis dimensionality reduction technique to get a more compact, robust, and discriminative descriptor. For face recognition, the efficiency of our algorithm is proved by strong results obtained on both constrained (Face Recognition Technology, FERET) and unconstrained (Labeled Faces in the Wild, LFW) data sets in addition with the low complexity. Impressively, our algorithm is about 30 times faster than those based on Gabor filters. Furthermore, by proposing an additional technique that makes our descriptor robust to rotation, we validate its efficiency for the task of image matching.