Merge pull request #37 from uic-evl/news

news articles

Author: renambot-uic

_news/2025/2025-11-02-2952.md

@@ -0,0 +1,23 @@
+---
+layout: post
+title: BioMedVis 2025 Accepted Papers
+date: 2025-11-02
+tags: event
+categories: events
+tabs: true
+image: biovislogo.png-srcw.jpg
+
+---
+
+EVL PhD candidate Hossein Fathollahian has his short paper accepted at the <ahref="http://biovis.net/2025/">BioMedVis Challenge 2025</a>, a data contest organized by <a href="https://ieeevis.org/year/2025/welcome">IEEE VIS 2025</a>.<br><br>
+H. Fathollahian, S. Zhao, M. Nipu, G.E. Marai, &ldquo;Attention-based Region of Interest Discovery in 3D Tissue Images&rdquo;, IEEE BioMedVis Challenge 2025, IEEE VIS, 2025.<br><br>
+This work presents an approach for the detection and exploration of regions of interest (ROIs) using graph-based analysis. The novel computational approach for ROI detection is a main strength, and the interactive frontend includes several features addressing the challenge tasks, such as quick navigation to prominent ROIs.<br><br>
+Additionally, Hossein paper &ldquo;From Peaks to Patterns&rdquo; was accepted to the ReDesign Contest:<br><br>
+Authors: Hossein Fathollahian, Marziye Salahshour<br><br>
+<strong>Abstract:</strong> MRS is a non-invasive way of gaining information about cellular metabolism and can be used to study neurological and metabolic disorders. However, the clinical utility is limited by low signal quality, overlapping spectral peaks, and the inability to interpret and compare metabolite data. We developed an interactive visual analysis system that establishes the methods by adding filtering, quality measurement, and a comparative summary of MRS signals. This will allow researchers to perform a more accurate evaluation of spectral data, which will help them understand the molecular processes behind the complex neurological and metabolic processes in greater depth.<br><br>
+Congratulations, Hossein!
+
+![image](/images/biovislogo.png-srcw.jpg
+){:style="max-width: 100%"}
+
+Link: [http://biovis.net/2025/](http://biovis.net/2025/)

_news/2025/2025-11-02-2963.md

@@ -0,0 +1,25 @@
+---
+layout: post
+title: 'Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator'
+date: 2025-11-02
+tags: paper
+categories: papers
+tabs: true
+image: hnpredictor_teaser.png-srcw.jpg
+
+---
+
+## Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator
+**Nipu, N., van Dijk, L.,  Canahuate, G., Fuller, C.D., Marai, G.E.**
+- Location: Vienna, Austria
+- Link: [https://visualanalyticshealthcare.github.io/homepage/2025/](https://visualanalyticshealthcare.github.io/homepage/2025/)
+- PDF: [nafiul_hn_predictor_vahc.pdf](/documents/nafiul_hn_predictor_vahc.pdf)
+
+[![image](/images/hnpredictor_teaser.png-srcw.jpg
+){:style="max-width: 100%"}](/images/hnpredictor_teaser.png-srcw.jpg
+)
+- Caption: Interactive Oncological Risk Estimator
+
+In the precision medicine paradigm, oncological treatment leverages complex ensemble datasets of similar patients to estimate the outcomes for a current patient. A key challenge is developing and deploying easy-to-understand AI predictive models for the outcomes of a specific patient, based on patient data from multiple
+institutions. We describe the lessons learned from the development and deployment of an interactive dashboard to support the analysis of individual head and neck cancer patient outcomes based on cohort data. As required by the project, the dashboard design aims to handle a large client base. The dashboard combines an AI solution with a multi-view interface featuring domain-specific plots to facilitate the visual analysis of patient outcomes and to quickly stratify new patients into risk groups. A year after the successful public deployment of the dashboard, we evaluate it with clinician domain experts. We report the feedback and we reflect on the lessons learned through this experience.<br><br>
+<strong>Index Terms:</strong> VA-machine intelligence for healthcare data visualization, Human-centered AI for health decision-making, Dashboard, Risk Stratification, Precision Medicine