2 research outputs found
Explainable 2D Vision Models for 3D Medical Data
Training Artificial Intelligence (AI) models on three-dimensional image data
presents unique challenges compared to the two-dimensional case: Firstly, the
computational resources are significantly higher, and secondly, the
availability of large pretraining datasets is often limited, impeding training
success. In this study, we propose a simple approach of adapting 2D networks
with an intermediate feature representation for processing 3D volumes. Our
method involves sequentially applying these networks to slices of a 3D volume
from all orientations. Subsequently, a feature reduction module combines the
extracted slice features into a single representation, which is then used for
classification. We evaluate our approach on medical classification benchmarks
and a real-world clinical dataset, demonstrating comparable results to existing
methods. Furthermore, by employing attention pooling as a feature reduction
module we obtain weighted importance values for each slice during the forward
pass. We show that slices deemed important by our approach allow the inspection
of the basis of a model's prediction
Türkiye’de Uzakdoğu mutfağının Quick China örneği üzerinden incelenmesi
Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2015.This work is a student project of the The Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.by Özer, Abdürrahim