
Dealing with opioid addiction and its long-term
consequences is of great importance, as the addiction to opioids is
emerged gradually, and established strongly in a given patient’s
body. Based on recent research, quitting the opioid requires
clinicians to arrange a gradual plan for the patients who deal
with the difficulties of overcoming addiction. This, in turn,
necessitates observing the patients’ wellness periodically, which
is conventionally made by setting clinical appointments. With
the advent of wearable sensors continuous patient monitoring
becomes possible. However, the data collected through the sensors
is pervasively noisy, where using sensors with different sampling
frequency challenges the data processing. In this work, we handle
this problem by using data from cognitive tests, along with heart
rate (HR) and heart rate variability (HRV). The proposed recipe
enables us to interpret the data as a feature space, where we can
predict the wellness of the opioid patients by employing extreme
gradient boosting (XGBoost), which results in 96.12% average
accuracy of prediction as the best-achieved performance.

XGBoost to Interpret the Opioid Patients’ State Based on Cognitive and Physiological Measures


Medical image segmentation is a critical field in the
domain of computer vision and with the growing acclaim of deep
learning based models, research in this field is constantly expanding.
Optical coherence tomography (OCT) is a non-invasive
method that scans the human’s retina with depth. It has been
hypothesized that the thickness of the retinal layers extracted
from OCTs could be an efficient and effective biomarker for
early diagnosis of AD. In this work, we aim to design a selftraining
model architecture for the task of segmenting the retinal
layers in OCT scans. Neural architecture search (NAS) is a
subfield of AutoML domain, which has a significant impact on
improving the accuracy of machine vision tasks. We integrate
the NAS algorithm with a Unet auto-encoder architecture as
its backbone. Then, we employ our proposed model to segment
the retinal nerve fiber layer in our preprocessed OCT images
with the aim of AD diagnosis. In this work, we trained a
super-resolution generative adversarial network on the raw OCT
scans to improve the quality of the images before the modeling
stage. In our architecture search strategy, different primitive
operations suggested to find down- & up-sampling Unet cell
blocks and the binary gate method has been applied to make
the search strategy more practical. Our architecture search
method is empirically evaluated by training on the Unet and
NAS-Unet from scratch. Specifically, the proposed NAS-Unet
training significantly outperforms the baseline human-designed
architecture by achieving 95.1% in the mean Intersection over
Union metric and 79.1% in the Dice similarity coefficient.

OCT Image Segmentation Using Neural Architecture Search and SRGAN

PS T5.1: Image and Audio Processing

poster

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. 


Omid Dehzangi

2

SHORT BIO

Presentations

XGBoost to Interpret the Opioid Patients’ State Based on Cognitive and Physiological Measures

OCT Image Segmentation Using Neural Architecture Search and SRGAN

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