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10/24/2022
Iterative Patch Selection for High-Resolution Image Recognition
High-resolution images are prevalent in various applications, such as au...
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09/29/2022
Training Normalizing Flows from Dependent Data
Normalizing flows are powerful non-parametric statistical models that fu...
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07/02/2022
Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation
Since labeling medical image data is a costly and labor-intensive proces...
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12/17/2021
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays
We propose a simple and efficient image classification architecture base...
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11/26/2021
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics
High annotation costs are a substantial bottleneck in applying modern de...
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09/29/2021
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout
Self-supervised learning methods can be used to learn meaningful represe...
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09/16/2021
Explainability Requires Interactivity
When explaining the decisions of deep neural networks, simple stories ar...
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06/06/2020
3D Self-Supervised Methods for Medical Imaging
Self-supervised learning methods have witnessed a recent surge of intere...
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05/22/2020
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
Deep Gaussian Processes learn probabilistic data representations for sup...
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12/11/2019
Multimodal Self-Supervised Learning for Medical Image Analysis
In this paper, we propose a self-supervised learning approach that lever...
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10/14/2019
Two-sample Testing Using Deep Learning
We propose a two-sample testing procedure based on learned deep neural n...
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12/02/2018
Integrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimer's disease
For precision medicine and personalized treatment, we need to identify p...
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05/03/2012