Italy

ICPR 2020

Hybrid Loss with Network Trimming for Disease Recognition in Gastrointestinal Endoscopy

computer-assisted technology

endoscopy

object detection

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


technical paper

The Fusion of Neural Architecture Search and Destruction and Construction Learning

Overview of the ICPR 2020 Competition on HArvesting Raw Tables from Infographics

Axis analysis on Harvesting Raw Tables from Infographics

Text recognition and role classification from raw chart based on cascade object detection and graph neural network

Detection-based methods for legend analysis and raw tables harvest from infographics document

Award ceremony and closing

The EndoTect 2020 Challenge: Evaluation and Comparison of Classification, Segmentation and Inference Time for Endoscopy

A Hierarchical Multi-Task Approach to Gastrointestinal Image Analysis

Delving into High Quality Endoscopic Diagnoses

Medical Diagnostic by Data Bagging for Various Instances of Neural Network

https://d3smihljt9218e.cloudfront.net/transcript/9589/subtitle/de3af4dc739ea643513261bba246a731.vtt

English (automatic)

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Hybrid Loss with Network Trimming for Disease Recognition in Gastrointestinal Endoscopy

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