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Found 6 papers, 0 papers with code
Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures
Two key issues are identified: 1) the curse of dimensionality when applying these measures to raw data, and 2) the unreliable comparison of clustering results across different embedding spaces stemming from variations in training procedures and parameter settings in different clustering models.
SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation
However, direct transfer across datasets from different domains may lead to poor performance for CNN due to two issues, the large domain shift present in the biomedical imaging datasets and the extremely small scale of the COVID-19 chest x-ray dataset.
Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
In this work, we propose a novel framework that exploits the structure information between and within report sections for generating CXR imaging reports.
Adversarial Domain Adaptation Being Aware of Class Relationships
In this paper, we propose a novel relationship-aware adversarial domain adaptation (RADA) algorithm, which first utilizes a single multi-class domain discriminator to enforce the learning of inter-class dependency structure during domain-adversarial training and then aligns this structure with the inter-class dependencies that are characterized from training the label predictor on source domain.
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images
Motivated by the zoom-in operation of a pathologist using a digital microscope, RAZN learns a policy network to decide whether zooming is required in a given region of interest.
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
Specifically, we propose a model that enforces our intuition that prediction masks should be domain independent.
