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Found 23 papers, 11 papers with code
Foundation Model of Electronic Medical Records for Adaptive Risk Estimation
We processed 299, 721 unique patients from MIMIC-IV into 285, 622 PHTs, with 60% including hospital admissions.
TabMixer: Noninvasive Estimation of the Mean Pulmonary Artery Pressure via Imaging and Tabular Data Mixing
We test TabMixer for mPAP estimation and show that it enhances the performance of Convolutional Neural Networks, 3D-MLP and Vision Transformers while being competitive with previous modules for imaging and tabular data.
Zero Shot Health Trajectory Prediction Using Transformer
Integrating modern machine learning and clinical decision-making has great promise for mitigating healthcare's increasing cost and complexity.
Let Me DeCode You: Decoder Conditioning with Tabular Data
When labels are not available during inference, our model infers the necessary conditioning embedding directly from the input data, thanks to a feed-forward network learned during the training phase.
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
MISS: Multiclass Interpretable Scoring Systems
In this work, we present a novel, machine-learning approach for constructing Multiclass Interpretable Scoring Systems (MISS) - a fully data-driven methodology for generating single, sparse, and user-friendly scoring systems for multiclass classification problems.
Noninvasive Estimation of Mean Pulmonary Artery Pressure Using MRI, Computer Models, and Machine Learning
We show using the ablation study, that physics-informed feature engineering based on models of blood circulation increases the performance of Gradient Boosting Decision Trees-based algorithms for classification of PH and regression of values of mPAP.
Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings?
Automatic detection and tracking of emotional states has the potential for helping individuals with various mental health conditions.
TabAttention: Learning Attention Conditionally on Tabular Data
In this paper, we introduce TabAttention, a novel module that enhances the performance of Convolutional Neural Networks (CNNs) with an attention mechanism that is trained conditionally on tabular data.
Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load
The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement.
Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation
Cardiac Magnetic Resonance Imaging is commonly used for the assessment of the cardiac anatomy and function.
Virtual Reality Simulator for Fetoscopic Spina Bifida Repair Surgery
Spina Bifida (SB) is a birth defect developed during the early stage of pregnancy in which there is incomplete closing of the spine around the spinal cord.
Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings
For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips.
Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements
We found that automated fetal biometric measurements obtained by FUVAI were comparable to the measurements performed by experienced sonographers The observed differences in measurement values were within the range of inter- and intra-observer variability.
BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video
Predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of delivery.
POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 Detection
We show that our proposed method is more robust than previous attempts to counter confounding factors such as ECG leads in chest X-rays that often influence model classification decisions.
FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements
The main goal in fetal ultrasound scan video analysis is to find proper standard planes to measure the fetal head, abdomen and femur.
Artificial Intelligence in PET: an Industry Perspective
Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications.
Pneumothorax and chest tube classification on chest x-rays for detection of missed pneumothorax
Chest x-ray imaging is widely used for the diagnosis of pneumothorax and there has been significant interest in developing automated methods to assist in image interpretation.
Deep Learning based detection of Acute Aortic Syndrome in contrast CT images
Acute aortic syndrome (AAS) is a group of life threatening conditions of the aorta.
Human Gist Processing Augments Deep Learning Breast Cancer Risk Assessment
Radiologists can classify a mammogram as normal or abnormal at better than chance levels after less than a second's exposure to the images.
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples.
Learning the Sparse and Low Rank PARAFAC Decomposition via the Elastic Net
In this article, we derive a Bayesian model to learning the sparse and low rank PARAFAC decomposition for the observed tensor with missing values via the elastic net, with property to find the true rank and sparse factor matrix which is robust to the noise.
