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Posters - Schedules

Poster presentations at ISMB/ECCB 2021 will be presented virtually. Authors will pre-record their poster talk (5-7 minutes) and will upload it to the virtual conference platform site along with a PDF of their poster beginning July 19 and no later than July 23. All registered conference participants will have access to the poster and presentation through the conference and content until October 31, 2021. There are Q&A opportunities through a chat function and poster presenters can schedule small group discussions with up to 15 delegates during the conference.

Information on preparing your poster and poster talk are available at: https://www.iscb.org/ismbeccb2021-general/presenterinfo#posters

Ideally authors should be available for interactive chat during the times noted below:

View Posters By Category

Session A: Sunday, July 25 between 15:20 - 16:20 UTC
Session B: Monday, July 26 between 15:20 - 16:20 UTC
Session C: Tuesday, July 27 between 15:20 - 16:20 UTC
Session D: Wednesday, July 28 between 15:20 - 16:20 UTC
Session E: Thursday, July 29 between 15:20 - 16:20 UTC
Assessing the role of Digital Device Technology in Alzheimer’s Disease using Artificial Intelligence
COSI: TransMed
  • Holger Fröhlich, Fraunhofer SCAI and University of Bonn, Germany
  • Meemansa Sood, Fraunhofer SCAI and University of Bonn, Germany
  • Mohamed Aborageh, Fraunhofer SCAI and University of Bonn, Germany
  • Robbert Harms, Altoida Inc.* 2100 West Loop South, Suite 1450, Houston, TX, 77027 - USA, United States
  • Maximilian Bügler, Altoida Inc.* 2100 West Loop South, Suite 1450, Houston, TX, 77027 - USA, United States
  • Ioannis Tarnanas, Altoida Inc.* 2100 West Loop South, Suite 1450, Houston, TX, 77027 - USA, United States

Short Abstract: In Alzheimer’s Disease (AD) the use of digital technologies has gained a lot of attention, because it may help to diagnose the disease in a pre-symptomatic stage. However, before any use in clinical routine, digital measures (DMs) need to be evaluated carefully by assessing their relationship to established clinical scores and understanding their diagnostic benefit. Along these lines, the IMI project RADAR-AD (www.radar-ad.org) evaluates a smartphone based virtual reality game panel that can help to assess cognitive impairment. In our work we applied Variational Autoencoder Modular Bayesian Network (VAMBN) [1] on the virtual reality game data and analysed connections between DMs and cognitive assessments (e.g. Mini Mental State Examination). Based on our model we then predicted DMs within the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. This resulted into a network that allowed us to disentangle and quantify the relationship between DMs, established clinical scores, brain volumes as well as molecular mechanisms. Therefore, DMs may have the potential to act as a vital measure in the diagnosis of AD in a pre-symptomatic stage.

[1] Gootjes-Dreesbach L, Sood M…..Fröhlich H (2020) Variational Autoencoder Modular Bayesian Networks for Simulation of Heterogeneous Clinical Study Data. Front. Big Data 3:16. doi: 10.3389/fdata.2020.00016

HISTOPATHOLOGICAL IMAGE ANALYSIS FOR ORAL SQUAMOUS CELL CARCINOMA CLASSIFICATION USING CONCATENATED DEEP LEARNING MODELS
COSI: TransMed
  • Faisal Khan, Institute of Integrative Biosciences, CECOS University, Phase VI, Hayatabad, Peshawar, Pakistan, Pakistan
  • Ibrar Amin, Precision Medicine Lab, Rehman Medical Institute, Phase V, Hayatabad, Peshawar, Pakistan, Pakistan
  • Hina Zamir, Precision Medicine Lab, Rehman Medical Institute, Phase V, Hayatabad, Peshawar, Pakistan, Pakistan

Short Abstract: Oral squamous cell carcinoma (OSCC) is a subset of head and neck cancer (HNSCC), the seventh most common cancer worldwide, and accounts for more than ninety percent of oral malignancies. Early detection of OSCC is essential for effective treatment and reducing the mortality rate. However, the gold standard method of microscopy-based histopathological investigation is often challenging, time-consuming and relies on human expertise. Automated analysis of oral biopsy images can aid the histopathologists in performing a rapid and arguably more accurate diagnosis of OSCC. In this study, we present deep learning (DL) based automated classification of 290 normal and 934 cancerous oral histopathological images published by Tabassum et al (Data in Brief, 2020). We utilized a transfer learning approach by adapting three pre-trained DL models to OSCC detection. VGG16, InceptionV3, and Resnet50 were fine-tuned individually and then used in concatenation as feature extractors. The concatenated model outperformed the individual models and achieved 96.66% accuracy (95.16% precision, 98.33% recall, and 95.00% specificity) compared to 89.16% (VGG16), 94.16% (InceptionV3) and 90.83% (ResNet50). These results demonstrate that the concatenated model can effectively replace the use of a single DL architecture.



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