Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification

We present an exhaustive investigation of recent Deep Learning architectures, algorithms, and strategies for the task of document image classification to finally reduce the error by more than half. Existing approaches, such as the DeepDocClassifier, apply standard Convolutional Network architectures with transfer learning from the object recognition domain. The contribution of the paper is threefold: First, it investigates recently introduced very deep neural network architectures (GoogLeNet, VGG, ResNet) using transfer learning (from real images). Second, it proposes transfer learning from a huge set of document images, i.e. 400,000 documents. Third, it analyzes the impact of the amount of training data (document images) and other parameters to the classification abilities. We use two datasets, the Tobacco-3482 and the large-scale RVL-CDIP dataset. We achieve an accuracy of 91.13% for the Tobacco-3482 dataset while earlier approaches reach only 77.6%. Thus, a relative error reduction of more than 60% is achieved. For the large dataset RVL-CDIP, an accuracy of 90.97% is achieved, corresponding to a relative error reduction of 11.5%

Intraperitoneal Pre-Insufflation of 0.125% Bupivaciane with Tramadol for Postoperative Pain Relief Following Laparoscopic Cholecystectomy

Objective: To compare the efficacy of intraperitoneal pre-insufflation of combined 0.125% bupivacaine and tramadol with bupivacaine alone in controlling postoperative pain among patients undergoing laparoscopic cholecystectomy.  Study Design: Randomized Controlled Trial   Place and Duration: The study was conducted at department of surgery, Holy Family, hospital, Rawalpindi from November 2016 to December 2017.  Methodology: Patients of either gender with ASA-1 and ASA-2 undergoing elective laparoscopic cholecystectomy were randomly divided into two groups of 50 each, by random number table method. The patients received the study drugs at the initiation of insufflation of CO2 in the intraperitoneal space by the operating surgeon under laparoscopic camera guidance over the gallbladder bed, tramadol 2 mg/kg in 30 ml of 0.125% bupivacaine was instilled in the gall bladder fossa under direct laparoscopic control in (study group) group A patients while group B patients received bupivacaine 30 ml of 0.125% solution only. Simultaneously, each group was assessed for intensity of pain at rest through VAS at 1, 4, 12 and 24 hrs after surgery.  Results: The mean age of group A (intervention group) was 43.25 ± 8.56 years and of group B (control group) was 44.89 ± 7.65 years. There were 24 (48%) male patients in group A and 29 (58%) in group B. In intervention group 34 (68%) patients and in control group 38 (76%) patients presented with ASA-I. The intervention group (Group A) had significantly (p-value < 0.05) lower mean values at 1 hour (3.89 ± 1.24 vs 5.79 ± 1.35), 4 hours (2.76 ± 2.13 vs 4.27 ± 1.08), 12 hours (2.28 ± 1.05 vs 4.89 ± 0.95) and 24 hours (2.16 ± 0.89 vs 3.23 ± 0.79) as compared with control group (Group B). The analysis showed that there was no statistically significant (p-value > 0.05) difference in side effects like nausea, vomiting, and shivering between both groups A and B.  Conclusion: Intraperitoneal instillation of bupivacaine plus tramadol reduces not only the intensity of postoperative pain but also the total rescue analgesic dose consumption after LC.&nbsp

2D Object Detection with Transformers: A Review

Astounding performance of Transformers in natural language processing (NLP) has delighted researchers to explore their utilization in computer vision tasks. Like other computer vision tasks, DEtection TRansformer (DETR) introduces transformers for object detection tasks by considering the detection as a set prediction problem without needing proposal generation and post-processing steps. It is a state-of-the-art (SOTA) method for object detection, particularly in scenarios where the number of objects in an image is relatively small. Despite the success of DETR, it suffers from slow training convergence and performance drops for small objects. Therefore, many improvements are proposed to address these issues, leading to immense refinement in DETR. Since 2020, transformer-based object detection has attracted increasing interest and demonstrated impressive performance. Although numerous surveys have been conducted on transformers in vision in general, a review regarding advancements made in 2D object detection using transformers is still missing. This paper gives a detailed review of twenty-one papers about recent developments in DETR. We begin with the basic modules of Transformers, such as self-attention, object queries and input features encoding. Then, we cover the latest advancements in DETR, including backbone modification, query design and attention refinement. We also compare all detection transformers in terms of performance and network design. We hope this study will increase the researcher's interest in solving existing challenges towards applying transformers in the object detection domain. Researchers can follow newer improvements in detection transformers on this webpage available at: https://github.com/mindgarage-shan/trans_object_detection_surve