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Found 16 papers, 5 papers with code
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.
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.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
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.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Continuous-Time Deep Glioma Growth Models
The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy.
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data
Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points.
The Federated Tumor Segmentation (FeTS) Challenge
The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.
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.
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients
Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions.
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection
Variational Auto-Encoders have often been used for unsupervised pretraining, feature extraction and out-of-distribution and anomaly detection in the medical field.
Unsupervised Anomaly Localization using Variational Auto-Encoders
An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist.
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
Unsupervised learning can leverage large-scale data sources without the need for annotations.
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
The U-Net was presented in 2015.
Ranked #2 on
Medical Image Segmentation
on Synapse multi-organ CT
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning
In this paper, we propose the re-colorization of medical images with a generative adversarial network (GAN)-based architecture as auxiliary task.
