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Found 10 papers, 2 papers with code
GLFNET: Global-Local (frequency) Filter Networks for efficient medical image segmentation
We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance.
Riemannian Prediction of Anatomical Diagnoses in Congenital Heart Disease based on 12-lead ECGs
Congenital heart disease (CHD) is a relatively rare disease that affects patients at birth and results in extremely heterogeneous anatomical and functional defects.
Controllable Chest X-Ray Report Generation from Longitudinal Representations
Previous approaches to automated radiology reporting generally do not provide the prior study as input, precluding comparison which is required for clinical accuracy in some types of scans, and offer only unreliable methods of interpretability.
Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting
Automated approaches to radiology reporting require the image to be encoded into a suitable token representation for input to the language model.
Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation
Semi-supervised learning has demonstrated great potential in medical image segmentation by utilizing knowledge from unlabeled data.
Towards personalised music-therapy; a neurocomputational modelling perspective
In particular, the direct interaction of auditory with the motor and the reward system via a predictive framework explains the efficacy of music-based interventions in motor rehabilitation.
Optimizing Vision Transformers for Medical Image Segmentation
For medical image semantic segmentation (MISS), Vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture long-range correlations.
Adversarial Attention for Human Motion Synthesis
Furthermore, we show that we can improve the classification performance of deep learning models in cases where there is inadequate real data, by supplementing existing datasets with synthetic motions.
Towards Explainable, Privacy-Preserved Human-Motion Affect Recognition
We evaluate the effectiveness of our method to existing methods at recognizing emotions using both 3D temporal joint signals and manually extracted features.
Artificial Intelligence and Robotics
The recent successes of AI have captured the wildest imagination of both the scientific communities and the general public.
