Paper tackles medical question similarity using domain-relevant embeddings.
problem Identifying same-question pairs in medical contexts.
method Semi-supervised pre-training of a neural network on medical question-answer pairs.
result Our model achieves 82.6% accuracy on medical question similarity task.
Paper tackles medical image diagnosis with unsupervised domain adaptation.
problem Limited labeled samples and label noise in medical images.
method Collaborative Unsupervised Domain Adaptation (UDA) algorithm.
result Empirical results show superiority of the proposed method.
This work provides a strong baseline for the problem of multi-source multi-target domain adaptation and generalization in medical imaging. Using a diverse collection of ten chest X-ray datasets, we empirically demonstrate the benefits of training medical imaging deep learning models on varied patient populations for ge…
Research benchmarks LLMs in medical domain to reduce hallucinations.
problem Hallucinations in medical LLMs can lead to incorrect information.
method Developed Med-HALT dataset and testing methods.
result Significant performance differences among LLMs identified.
Proposes first method for continuously indexed domain adaptation.
problem Challenges of transferring knowledge between continuously indexed domains.
method Combines adversarial adaptation with a novel discriminator.
result Outperforms state-of-the-art methods on synthetic and real-world datasets.
Proposes a new method for medical diagnosis using network-based representation learning.
problem Improving medical diagnosis accuracy through better data representation.
method Heterogeneous network-based model and modified metapath2vec algorithm for learning latent node representations.
result Significant performance boost in symptom/disease classification and disease prediction tasks.
Improves reliability of medical diagnosis uncertainty estimates.
problem Label uncertainty in medical diagnosis.
method Post-hoc alpha-calibration method for neural network classifiers. result Significantly enhances reliability of uncertainty estimates.
Unified deep learning predicts Parkinson's disease from medical images.
problem Diagnosing Parkinson's disease accurately from medical images.
method Transfer learning and domain adaptation using deep convolutional and recurrent neural networks.
result The approach effectively predicts Parkinson's disease across different medical environments.
The paper explores how neural networks generalize differently from natural and medical images.
problem Discrepancies in generalization error between natural and medical images.
method Established and empirically validated a generalization scaling law with respect to intrinsic dataset properties.
result Higher intrinsic 'label sharpness' of medical images leads to higher adversarial vulnerability.
Harmonization schemes limit accuracy due to domain information.
problem Harmonization schemes lead to inaccurate predictions due to domain information.
method Analysis of mutual information and real label value informativeness.
result Accuracy is limited by the domain with least information.
This paper benchmarks OoDD methods for medical imaging.
problem Medical models trained for one domain may fail on images from a different domain.
method Defined 3 categories of OoD examples and benchmarked methods in 3 medical imaging domains.
result Simple binary classifier on feature representation yields best accuracy and AUPRC.
Tensor networks improve medical image classification performance.
problem Improving medical image classification accuracy.
method Extending tensor networks to medical image analysis, focusing on 2D images.
result Tensor networks achieve comparable performance to deep learning methods with fewer hyperparameters and resources.
Study reveals differences in medical image models' hidden representation refinement.
problem Understanding how intrinsic dimensionality changes in neural network hidden representations across different domains.
method Analysis of 11 natural and medical image datasets using 6 network architectures.
result Medical image models refine hidden representations earlier, suggesting differences in feature abstraction.
This work improves medical image segmentation with limited annotations using contrastive learning.
problem Lack of labeled data for medical image segmentation.
method Contrastive learning framework for semi-supervised segmentation with domain-specific and problem-specific cues.
result Significant improvements in segmentation performance compared to other methods.
A new metric FRD improves comparing medical images.
problem Comparing medical images for distribution or domain differences.
method Developed a new metric FRD using standardized radiomic features.
result FRD outperforms other metrics in various medical imaging applications.
Enhances uncertainty estimation in medical image segmentation.
problem Frequency-related noise in medical imaging leads to biased uncertainty estimates.
method Extends MC-Dropout to the frequency domain for better uncertainty estimation.
result MC-Frequency Dropout improves calibration and uncertainty in semantic segmentation.
Survey of deep learning methods for medical anomaly detection.
problem Medical anomaly detection using machine learning.
method Thorough review of deep learning techniques across various medical domains.
result Comparison and contrast of deep learning models and their limitations.
Dynamic memory prevents forgetting in continuous learning of medical images.
problem Catastrophic forgetting in machine learning models over time due to domain shifts.
method Dynamic memory to store and replay diverse training data subsets.
result Dynamic memory mitigates forgetting without knowing when shifts occur.
The discovery of adversarial examples has raised concerns about the practical deployment of deep learning systems. In this paper, we demonstrate that adversarial examples are capable of manipulating deep learning systems across three clinical domains. For each of our representative medical deep learning classifiers, bo…
Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement lear…
The study evaluates AI model performance measures for medical use.
problem Selecting appropriate performance measures for AI models in medical practice.
method Assessed 32 performance measures across five domains for binary outcomes.
result 17 measures are both proper and reflect decision-analytic performance.
The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translat…
Efficient equivariant MobileNetV2 for medical applications on mobile devices.
problem Limited computational resources for deep learning models in medical applications.
method Design and optimize an equivariant version of MobileNetV2 with model quantization.
result Achieved close-to state-of-the-art performance on medical dataset with improved efficiency.
FLBench automates federated learning benchmarking.
problem Manual dataset partitioning fails to simulate real-world isolated data islands.
method Develops a federated learning benchmark suite with three domains.
result Automates evaluation of federated learning systems and algorithms.
Paper shows how to quantify uncertainty in medical ML models.
problem Uncertainty in opaque ML models can lead to safety risks in medical applications.
method Introduces Uncertainty Wrapper to quantify uncertainty transparently.
result Demonstrates practical utility of Uncertainty Wrapper in flow cytometry.
Proposes a unified normalization method for multi-domain medical images.
problem Inadequate joint information across multiple datasets hinders image segmentation performance.
method Adversarial and task-driven normalization approach to learn a common normalizing function across multiple datasets.
result Jointly normalized images improve segmentation accuracy by up to 57.5%.
Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connecti…
Early detection of Alzheimer's disease (AD) and identification of potential risk/beneficial factors are important for planning and administering timely interventions or preventive measures. In this paper, we learn a disease model for AD that combines genotypic and phenotypic profiles, and cognitive health metrics of pa…
Risk adjustment has become an increasingly important tool in healthcare. It has been extensively applied to payment adjustment for health plans to reflect the expected cost of providing coverage for members. Risk adjustment models are typically estimated using linear regression, which does not fully exploit the informa…
Underspecified ML models can behave unpredictably in real-world use.
problem ML models can fail in real-world deployment due to ambiguous predictors.
method Identified underspecification as the cause, showing it affects various ML domains.
result Underspecified models can behave differently in deployment domains.
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existi…
Deep learning has been successfully applied to a variety of image classification tasks. There has been keen interest to apply deep learning in the medical domain, particularly specialties that heavily utilize imaging, such as ophthalmology. One issue that may hinder application of deep learning to the medical domain is…
Deep learning improves solving medical imaging problems with sparse data.
problem Solving underdetermined inverse problems in medical imaging.
method Analyzing the structure of training data suitable for deep learning to solve highly non-linear underdetermined systems.
result Deep learning can learn reconstruction maps from training data for highly underdetermined systems.
FEET protocol evaluates foundation models across three scenarios.
problem Lack of standardized evaluation protocols for foundation models.
method Structured evaluation protocol across three use cases: frozen, few-shot, and fine-tuned embeddings.
result Demonstrates comprehensive assessment of foundation models' effectiveness.
ChatGPT's medical response accuracy is 56%, but studies vary widely.
problem Lack of standard guidelines for evaluating ChatGPT's performance in medicine.
method Systematic review and meta-analysis of 17 studies.
result ChatGPT's overall integrated accuracy in medical queries is 56%.
This paper tackles label noise in deep learning for medical image analysis.
problem Label noise impacts deep learning models in medical image analysis.
method Review of state-of-the-art techniques and experiments with label noise in medical datasets.
result Developed new methods to combat label noise in deep models for medical image analysis.
Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. Inspired by recent work in …
Predicting highrisk vascular diseases is a significant issue in the medical domain. Most predicting methods predict the prognosis of patients from pathological and radiological measurements, which are expensive and require much time to be analyzed. Here we propose deep attention models that predict the onset of the hig…
Study compares DL models for medical image segmentation, finds synergistic ensemble strategies improve performance.
problem Improving DL models for specialized medical image segmentation using transfer learning.
method Detailed comparisons of TII and LMI models for binary segmentation of medical images.
result Ensemble strategies improve performance by 10% in certain scenarios.
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the medical domain remains a challenge. In this paper, we present methods f…
New deep learning method improves 4D Flow MRI super-resolution under domain shift.
problem Domain shift in low-resolution 4D Flow MRI data.
method Distributional deep learning framework for domain generalization.
result Framework significantly outperforms traditional methods in real data applications.
FIGS improves prediction performance while maintaining interpretability, especially in medical domains.
problem Lack of interpretability in machine learning models, particularly in high-stakes domains like medicine.
method Generalizes CART algorithm to grow multiple trees in summation, combining logical rules with addition.
result FIGS achieves state-of-the-art prediction performance and derives interpretable clinical decision instruments (CDIs).
Deep learning helps predict epileptic seizures from EEG data.
problem Challenging prediction of epileptic seizures in medical domain.
method Applied deep learning and machine learning models to EEG data.
result Improved prediction of epileptic seizures.
Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling medical image data requires expert knowledge. Collaboration between institutions could address this challenge, but sharing medical data to…
A TCL framework improves causal effect estimation in limited data.
problem Improving causal effect estimation accuracy in limited data.
method Transfer Learning (TCL) with ℓ1 regularization for nuisance models.
result Non-asymptotic recovery guarantees for exttt{ℓ1-TCL} in high-dimensional settings. Deep learning models are now used in many different industries, while in certain domains safety is not a critical issue in the medical field it is a huge concern. Not only, we want the models to generalize well but we also want to know the models confidence respect to its decision and which features matter the most. Ou…
This work abstracts deep neural networks into concept graphs for better interpretability in medical tasks.
problem Lack of interpretability in deep learning models, especially in medical domains.
method Developed a graphical representation of medical image processing models to understand concept-based reasoning.
result Extracted a concept-level graph that reveals the decision-making process of deep learning models.
PLIs improve classifier performance by fine-tuning latent representations.
problem Difficult interpretation of high-dimensional latent representations in neural networks.
method Back-propagation of manual changes to low-dimensional embeddings using t-distributed stochastic neighbourhood embeddings.
result Manual separation of class clusters in latent space enhances classifier performance.