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09/14/2023
RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement
Despite the remarkable success of deep learning systems over the last de...
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07/05/2023
Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency
Independently trained machine learning models tend to learn similar feat...
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04/10/2023
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model
Foundation models have taken over natural language processing and image ...
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01/05/2023
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization
Unsupervised anomaly detection in medical imaging aims to detect and loc...
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06/23/2021
Continuous-Time Deep Glioma Growth Models
The ability to estimate how a tumor might evolve in the future could hav...
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06/09/2021
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data
Neural Processes (NPs) are a family of conditional generative models tha...
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05/12/2021
The Federated Tumor Segmentation (FeTS) Challenge
This manuscript describes the first challenge on Federated Learning, nam...
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11/28/2019
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients
Through training on unlabeled data, anomaly detection has the potential ...
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11/27/2019
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection
Variational Auto-Encoders have often been used for unsupervised pretrain...
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07/04/2019
Unsupervised Anomaly Localization using Variational Auto-Encoders
An assumption-free automatic check of medical images for potentially ove...
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12/14/2018
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection
Unsupervised learning can leverage large-scale data sources without the ...
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09/27/2018
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
The U-Net was presented in 2015. With its straight-forward and successfu...
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11/27/2017
