Conv-adapter PEFT of MedNeXt on BraTS-Africa achieves 0.80 mean Dice, comparable to full fine-tuning (0.77) and better than training only on BraTS-Africa (0.72).
Introducing A Novel Method For Adaptive Thresholding In Brain Tumor Medical Image Segmentation
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abstract
One of the most significant challenges in the field of deep learning and medical image segmentation is to determine an appropriate threshold for classifying each pixel. This threshold is a value above which the model's output is considered to belong to a specific class. Manual thresholding based on personal experience is error-prone and time-consuming, particularly for complex problems such as medical images. Traditional methods for thresholding are not effective for determining the threshold value for such problems. To tackle this challenge, automatic thresholding methods using deep learning have been proposed. However, the main issue with these methods is that they often determine the threshold value statically without considering changes in input data. Since input data can be dynamic and may change over time, threshold determination should be adaptive and consider input data and environmental conditions.
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Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset
Conv-adapter PEFT of MedNeXt on BraTS-Africa achieves 0.80 mean Dice, comparable to full fine-tuning (0.77) and better than training only on BraTS-Africa (0.72).