187 papers with code • 28 benchmarks • 26 datasets
Medical image segmentation is the task of segmenting objects of interest in a medical image.
( Image credit: IVD-Net )
Learning segmentation from noisy labels is an important task for medical image analysis due to the difficulty in acquiring highquality annotations.
It focuses on the quality of predictions made on the unlabeled data of interest when they are included for optimization during training, rather than improving generalization.
Medical image segmentation plays an essential role in developing computer-assisted diagnosis and therapy systems, yet still faces many challenges.
The convolution part is applied for extracting the shallow spatial features to facilitate the recovery of the image resolution after upsampling.
First, we show higher correlation to using full data for training when testing on the external validation set using smaller proxy data than a random selection of the proxy data.
With the development of deep encoder-decoder architectures and large-scale annotated medical datasets, great progress has been achieved in the development of automatic medical image segmentation.
Hence, we propose a new spatial guided self-supervised clustering network (SGSCN) for medical image segmentation, where we introduce multiple loss functions designed to aid in grouping image pixels that are spatially connected and have similar feature representations.
Unlike the current literature on task-specific self-supervised pretraining followed by supervised fine-tuning, we utilize SSL to learn task-agnostic knowledge from heterogeneous data for various medical image segmentation tasks.
In this work, we explore biased and unbiased errors artificially introduced to brain tumour annotations on MRI data.