A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Survey of deep learning methods for medical anomaly detection.
This study applies neural models to automatically recognize medical entities from natural language.
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize imag…
Proposes guidelines for developing medical AI products.
Efficient equivariant MobileNetV2 for medical applications on mobile devices.
Paper shows how to quantify uncertainty in medical ML models.
The paper investigates deep neural networks for medical imaging applications, providing interpretable results.
Supervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical image analysis applications. However, the impact of label noise has not received sufficient attention. Recent studies have shown that label noise can significantly impact the p…
The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique. Identifying same-question pairs could enable questions to be answered more effectively. While many research efforts have focused on the problem of general questio…
Improves reliability of medical diagnosis uncertainty estimates.
This guide introduces machine learning for medical data analysis.
This paper tackles shape denoising in computer vision and medical imaging.
RL algorithms with medical integration improve personalized treatment recommendations.
Review of deep learning methods in medical image registration.
Develops a new RL algorithm for medical treatment regimes.
Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset,…
Survey on understanding neural networks for medical applications.
Zero-Shot Learning helps learn new concepts without examples, useful for COVID-19 diagnosis.
Recent years have witnessed the emergence of 3D medical imaging techniques with the development of 3D sensors and technology. Due to the presence of noise in image acquisition, registration researchers focused on an alternative way to represent medical images. An alternative way to analyze medical imaging is by underst…
DiffDenoise preserves fine structures in medical images using conditional diffusion models.
Paper explores Rashomon set models for more trustworthy medical conclusions.
3D dataset for intracranial aneurysms aids deep learning applications.
PAC-Bayesian method improves deep learning generalization in medical imaging.
Transfer learning from natural image datasets, particularly ImageNet, using standard large models and corresponding pretrained weights has become a de-facto method for deep learning applications to medical imaging. However, there are fundamental differences in data sizes, features and task specifications between natura…
Research benchmarks LLMs in medical domain to reduce hallucinations.
Novel IRL method identifies suboptimal medical decisions in ICU data.
A TCL framework improves causal effect estimation in limited data.
New benchmark evaluates BDL methods in medical retinopathy diagnosis.
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established…
Hierarchical-CPI improves variable importance measurement for medical data.
Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by the superior performance of deep learning methods on various computer vision problems, there has been increasing work applying deep learning t…
A new metric FRD improves comparing medical images.
Paper explores VRM for PSMLC with partially labeled medical images.
CycleMorph improves image registration by preserving topology with cycle consistency.
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…
ChatGPT's medical response accuracy is 56%, but studies vary widely.
Is it true that patients with similar conditions get similar diagnoses? In this paper we show NLP methods and a unique corpus of documents to validate this claim. We (1) introduce a method for representation of medical visits based on free-text descriptions recorded by doctors, (2) introduce a new method for clustering…
Evaluating the clinical similarities between pairwise patients is a fundamental problem in healthcare informatics. A proper patient similarity measure enables various downstream applications, such as cohort study and treatment comparative effectiveness research. One major carrier for conducting patient similarity resea…
Variational inference provides approximations to the computationally intractable posterior distribution in Bayesian networks. A prominent medical application of noisy-or Bayesian network is to infer potential diseases given observed symptoms. Previous studies focus on approximating a handful of complicated pathological…
Study compares DL models for medical image segmentation, finds synergistic ensemble strategies improve performance.
Paper reviews neurolinguistics and language technologies, emphasizing mutual enrichment.
We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, pr…
Proposes online learning for Hawkes processes with network structure and event interaction.
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…
Improves medication name inference for telemedicine and conversational agents.
Regular medical records are useful for medical practitioners to analyze and monitor patient health status especially for those with chronic disease, but such records are usually incomplete due to unpunctuality and absence of patients. In order to resolve the missing data problem over time, tensor-based model is suggest…
Framework harmonizes EHR data across institutions for better analysis.
Representation learning methods that transform encoded data (e.g., diagnosis and drug codes) into continuous vector spaces (i.e., vector embeddings) are critical for the application of deep learning in healthcare. Initial work in this area explored the use of variants of the word2vec algorithm to learn embeddings for m…