Srishti Gautam receiving award from Klas Henning Pettersen
Image:
Michael Kampffmeyer

Srishti Gautam receiving award from Klas Henning Pettersen

Srishti Gautam receives best poster award

Srishti Gautam, PhD student in Visual Intelligence, at UiT received the Best Poster award at the NORA annual conference 2022

Best poster award

We congratulate Srishti Gautam for her poster being voted to be the Best Poster at the NORA 2022 annual conference!

The poster was entitled "Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation" and is linked to the work done in her PhD project on advancing self-explainable deep-learning.

Srishti explains that the motivation for the work is to explore if a multi-source and label dataset favor learning from spurious artifacts, i.e, if text labels present in the  X-ray images can effect the disease classification. A second motivation is  how do self-explaining models deal with label-imbalanced datasets.

Deep neural networks can learn to exploit source-specific correlations when trained for classification by combining multi-source Chest X-Ray datasets. We hypothesize that this undesirable effect can be enforced by prevalence of a disease corresponding to a source/hospital. Therefore, we perform a thorough analysis of the effect of disease-imbalanced datasets from two hospitals for automated Pneumonia detection. The results show that a deep-learning model can behave as a hospital-detector instead of the intended Pneumonia-detector in the presence of high disease-imbalance. This further stresses the importance of using more faithful and transparent self-explaining models for automated medical diagnosis.

Visual intelligence Director Robert Jenssen Congratulates and expresses how proud he is to see the high-quality and hard work of our PhD student Srishti Gautam being acknowledged by the votes for the best poster.

The co-authors on the poster are: Marina M.-C. Hohne (Technical University of Berlin) Stine Hansen (UiT) Robert Jenssen (UiT), and Michael Kampffmeyer.

The work is done in the Visual Intelligence reseach centre and also supported by the German Ministry for Education and Research through the third-party funding project Explaining 4.0 (ref. 01IS20055).

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