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Found 11 papers, 3 papers with code
Defining Effective Engagement For Enhancing Cancer Patients' Well-being with Mobile Digital Behavior Change Interventions
Digital Behavior Change Interventions (DBCIs) are supporting development of new health behaviors.
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.
Personalising Digital Health Behaviour Change Interventions using Machine Learning and Domain Knowledge
We are developing a virtual coaching system that helps patients adhere to behavior change interventions (BCI).
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.
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.
CXR-FL: Deep Learning-Based Chest X-ray Image Analysis Using Federated Learning
Federated learning enables building a shared model from multicentre data while storing the training data locally for privacy.
Continual Class Incremental Learning for CT Thoracic Segmentation
In this work, we evaluate FT and LwF for class incremental learning in multi-organ segmentation using the publicly available AAPM dataset.
Paying Per-label Attention for Multi-label Extraction from Radiology Reports
Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain.
Teacher-Student chain for efficient semi-supervised histology image classification
Deep learning shows great potential for the domain of digital pathology.
Ontological attention ensembles for capturing semantic concepts in ICD code prediction from clinical text
We present a semantically interpretable system for automated ICD coding of clinical text documents.
