Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset
Journal:
arXiv
Published Date:
Dec 18, 2024
Abstract
Automating brain tumor segmentation using deep learning methods is an ongoing
challenge in medical imaging. Multiple lingering issues exist including
domain-shift and applications in low-resource settings which brings a unique
set of challenges including scarcity of data. As a step towards solving these
specific problems, we propose Convolutional adapter-inspired
Parameter-efficient Fine-tuning (PEFT) of MedNeXt architecture. To validate our
idea, we show our method performs comparable to full fine-tuning with the added
benefit of reduced training compute using BraTS-2021 as pre-training dataset
and BraTS-Africa as the fine-tuning dataset. BraTS-Africa consists of a small
dataset (60 train / 35 validation) from the Sub-Saharan African population with
marked shift in the MRI quality compared to BraTS-2021 (1251 train samples). We
first show that models trained on BraTS-2021 dataset do not generalize well to
BraTS-Africa as shown by 20% reduction in mean dice on BraTS-Africa validation
samples. Then, we show that PEFT can leverage both the BraTS-2021 and
BraTS-Africa dataset to obtain mean dice of 0.8 compared to 0.72 when trained
only on BraTS-Africa. Finally, We show that PEFT (0.80 mean dice) results in
comparable performance to full fine-tuning (0.77 mean dice) which may show PEFT
to be better on average but the boxplots show that full finetuning results is
much lesser variance in performance. Nevertheless, on disaggregation of the
dice metrics, we find that the model has tendency to oversegment as shown by
high specificity (0.99) compared to relatively low sensitivity(0.75). The
source code is available at
https://github.com/CAMERA-MRI/SPARK2024/tree/main/PEFT_MedNeXt
