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Found 11 papers, 4 papers with code
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5, 101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions.
Improving Deep Learning Optimization through Constrained Parameter Regularization
Unlike the uniform application of a single penalty, CPR enforces an upper bound on a statistical measure, such as the L2-norm, of individual parameter matrices.
RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement
Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision.
Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge
The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89. 5\%, 90. 9\%, and 88. 3\% on North American, European, and Asian cohorts, respectively.
Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency
Given an ensemble of independently trained models, this results in correlated predictions and common failure modes.
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model
Foundation models have taken over natural language processing and image generation domains due to the flexibility of prompting.
Transformer Utilization in Medical Image Segmentation Networks
Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation.
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation.
Ranked #1 on
Medical Image Segmentation
on AMOS
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization
By utilizing the representations of contrastive learning, we aim to fix the over-fixation on low-level features and learn more semantic-rich representations.
Frequency Decomposition in Neural Processes
Neural Processes are a powerful tool for learning representations of function spaces purely from examples, in a way that allows them to perform predictions at test time conditioned on so-called context observations.
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
The U-Net was presented in 2015.
