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Found 10 papers, 2 papers with code
A 3D deep learning classifier and its explainability when assessing coronary artery disease
Early detection and diagnosis of coronary artery disease (CAD) could save lives and reduce healthcare costs.
STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension
Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks.
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CT
This makes high-quality GANs useful for unsupervised anomaly detection in medical imaging.
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning
Our model demonstrated comparable and complementary performance to radiologists when interpreting challenging lung nodules and showed improved performance (AUC=88\%) against models utilizing single time-point data only.
CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning
Using human evaluation and automatic faithfulness metrics, we show that our model significantly reduces all kinds of factual errors on the dialogue summarization, SAMSum corpus.
Is MC Dropout Bayesian?
We question the properties of MC Dropout for approximate inference, as in fact MC Dropout changes the Bayesian model; its predictive posterior assigns $0$ probability to the true model on closed-form benchmarks; the multimodality of its predictive posterior is not a property of the true predictive posterior but a design artefact.
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification
Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results.
The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data
Convolutional Neural Networks (CNNs) are widely used for image classification in a variety of fields, including medical imaging.
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data
This mismatch is known as sampling bias.
Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection
We can compute a relative change in airway volume that may be useful for quantifying IPF disease progression.
