Fwd: 5th Year MS thesis Presentation Rohini Banerjee

[Artur Dubrawski]

fysa, Rohini's MS defense is on Tuesday next week.

Cheers,
Artur

---------- Forwarded message ---------
From: Tracy Farbacher <tt1b at andrew.cmu.edu>
Date: Tue, Apr 15, 2025 at 7:18 PM
Subject: 5th Year MS thesis Presentation Rohini Banerjee





*Rohini BanerjeeTuesday, April 22, 20253:00 PMNewell Simon Hall (NSH)
3001Uncertainty-Aware AI for Clinical Decision Support*

*Abstract:*
Building interpretable-by-design AI models that intuitively communicate
model uncertainty is vital to engendering physician and patient trust. We
develop uncertainty-guided deep learning systems for two pertinent
healthcare settings. Efficient intravascular access in trauma and critical
care is a high-stakes intervention affording minimal tolerance for error.
Autonomous needle insertion systems can be useful in austere environments
due to the lack of skilled medical personnel. However, inaccuracies in
vessel segmentation modeling can result in vessel damage and hemorrhage.
The risk can be mitigated via predictive uncertainty estimation to assess
model reliability. Thus, we introduce MSU-Net, a novel multistage approach
to semantic vessel segmentation in ultrasound images that combines the
predictive power of Monte Carlo networks and deep ensembles. We demonstrate
significant improvements, 27.7% over the state-of-the-art, while enhancing
model reliability through a 20.9% stronger discrimination in epistemic
uncertainty between correct and incorrect predictions.

Next, we investigate the robustness of predictive modeling in quantifying
the severity of rash manifestations associated with Cutaneous
Dermatomyositis (CDM), a rare and currently incurable autoimmune disorder.
Given the importance of telemedicine for remote disease monitoring and
timely intervention, we address challenges of data scarcity and patient
diversity by integrating a novel BERT-style self-supervised learning (SSL)
framework to image-based models. Pretrained via masked image modeling on
demographically diverse images, our model achieves over a 40% improvement
in fine-tuning performance on high-resolution in-clinic hand images from a
limited cohort of 23 CDM patients. We achieve 83% accuracy on a held-out
patient set, surpassing the clinical benchmark of 70–75% accuracy. To our
knowledge, this is the first work to integrate uncertainty estimation into
such architectures, enabling robustness under distributional shift in skin
tone unseen during fine-tuning.

Our contributions lay the groundwork for developing accurate, statistically
rigorous, clinically actionable deep learning models. Future work aims to
improve the interpretability of models for equitable clinical decision
support.

*Thesis Document:*
https://drive.google.com/file/d/1TnC6IcyDmTD6IzBpaSKsIsQX1f8Bkl0K/view?usp=sharing

*Thesis Committee:*
Artur W. Dubrawski (Chair)
László A. Jeni
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