2 research outputs found
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients
The prediction of pancreatic ductal adenocarcinoma therapy response is a
clinically challenging and important task in this high-mortality tumour entity.
The training of neural networks able to tackle this challenge is impeded by a
lack of large datasets and the difficult anatomical localisation of the
pancreas. Here, we propose a hybrid deep neural network pipeline to predict
tumour response to initial chemotherapy which is based on the Response
Evaluation Criteria in Solid Tumors (RECIST) score, a standardised method for
cancer response evaluation by clinicians as well as tumour markers, and
clinical evaluation of the patients. We leverage a combination of
representation transfer from segmentation to classification, as well as
localisation and representation learning. Our approach yields a remarkably
data-efficient method able to predict treatment response with a ROC-AUC of
63.7% using only 477 datasets in total
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