49 research outputs found
MGCN: Medical Relation Extraction Based on GCN
With the progress of society and the improvement of living standards, people pay more and more attention to personal health, and WITMED (Wise Information Technology of med) has occupied an important position. The relationship prediction work in the medical field has high requirements on the interpretability of the method, but the relationship between medical entities is complex, and the existing methods are difficult to meet the requirements. This paper proposes a novel medical information relation extraction method MGCN, which combines contextual information to provide global interpretability for relation prediction of medical entities. The method uses Co-occurrence Graph and Graph Convolutional Network to build up a network of relations between entities, uses the Open-world Assumption to construct potential relations between associated entities, and goes through the Knowledge-aware Attention mechanism to give relation prediction for the entity pair of interest. Experiments were conducted on a public medical dataset CTF, MGCN achieved the score of 0.831, demonstrating its effectiveness in medical relation extraction
Evaluation of Tunable Data Compression in Energy-Aware Wireless Sensor Networks
Energy is an important consideration in wireless sensor networks. In the current compression evaluations, traditional indices are still used, while energy efficiency is probably neglected. Moreover, various evaluation biases significantly affect the final results. All these factors lead to a subjective evaluation. In this paper, a new criterion is proposed and a series of tunable compression algorithms are reevaluated. The results show that the new criterion makes the evaluation more objective. Additionally it indicates the situations when compression is unnecessary. A new adaptive compression arbitration system is proposed based on the evaluation results, which improves the performance of compression algorithms
Joint Layout Analysis, Character Detection and Recognition for Historical Document Digitization
In this paper, we propose an end-to-end trainable framework for restoring
historical documents content that follows the correct reading order. In this
framework, two branches named character branch and layout branch are added
behind the feature extraction network. The character branch localizes
individual characters in a document image and recognizes them simultaneously.
Then we adopt a post-processing method to group them into text lines. The
layout branch based on fully convolutional network outputs a binary mask. We
then use Hough transform for line detection on the binary mask and combine
character results with the layout information to restore document content.
These two branches can be trained in parallel and are easy to train.
Furthermore, we propose a re-score mechanism to minimize recognition error.
Experiment results on the extended Chinese historical document MTHv2 dataset
demonstrate the effectiveness of the proposed framework.Comment: 6 pages, 6 figure
All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting
Recently, end-to-end text spotting that aims to detect and recognize text
from cluttered images simultaneously has received particularly growing interest
in computer vision. Different from the existing approaches that formulate text
detection as bounding box extraction or instance segmentation, we localize a
set of points on the boundary of each text instance. With the representation of
such boundary points, we establish a simple yet effective scheme for end-to-end
text spotting, which can read the text of arbitrary shapes. Experiments on
three challenging datasets, including ICDAR2015, TotalText and COCO-Text
demonstrate that the proposed method consistently surpasses the
state-of-the-art in both scene text detection and end-to-end text recognition
tasks.Comment: Accepted to AAAI202