arXiv:2606. 18496v1 Announce Type: cross Abstract: Correspondence is fundamentally relational: it seeks the unknown transformation between two observations of a common scene, not the content of either.
arXiv:2608.21147v1 Announce Type: new Abstract: The cyclic structure of physiological processes offers a natural prior for self-supervised representation learning, and the cardiac cycle provides a pa...
The paper introduces a self‑supervised method for detecting end‑diastole (ED) and end‑systole (ES) in echocardiography by constraining the latent motion to a single‑parameter orbit, effectively modeling cardiac phase as a one‑dimensional signal. This approach yields an interpretable representation that directly identifies ED and ES, improving ED localisation and matching ES performance compared to prior state‑of‑the‑art methods, while using fewer training epochs and a more constrained model. The method is trained on EchoNet‑Dynamic without annotations and the code is publicly available.
arXiv:2608.24027v1 Announce Type: new Abstract: 4D medical image interpolation aims to recover missing volumes from sparsely observed time points and is important for dynamic anatomical analysis in a...
arXiv:2609.34965v2 Announce Type: replace-cross Abstract: Many physiological time series, such as cardiac and brain recordings, exhibit cyclostationarity: their statistics vary periodically with an u...
arXiv:2607. 00955v1 Announce Type: cross Abstract: Implicit neural representations (INRs) are well suited to cardiac motion estimation, providing continuous, compact representations of motion fields.
QuantumBoostNet is a hybrid classical‑quantum architecture designed to improve accuracy in identifying cardiac ultrasound views. The model combines a classical backbone with two heads—one classical and a 10‑qubit quantum circuit—using an adaptive mixing parameter to switch between them during training. Experiments show that, even with limited qubits, QuantumBoostNet outperforms state‑of‑the‑art classical and hybrid models on cardiac ultrasound view identification, image classification benchmarks, and demonstrates robustness to noise.
QuantumBoostNet is a hybrid classical‑quantum architecture designed to improve accuracy in identifying cardiac ultrasound views. It combines a classical backbone with two heads—one classical and a 10‑qubit quantum circuit—trained in two stages with an adaptive mixing parameter that balances loss dynamics. Experiments show that, even with limited qubits, QuantumBoostNet outperforms state‑of‑the‑art classical and hybrid models on cardiac ultrasound view identification, image classification benchmarks, and demonstrates robustness to noise.
The paper introduces Recursive Uncertainty-Gated Image Registration (RUGI), an iterative refinement method that updates deformation fields predicted by learning-based registration models using a gating map. Two gating strategies are explored: an uncertainty-based approach and an image residual error approach, both concentrating updates on difficult regions. Experiments on cardiac MRI and echocardiography datasets show that RUGI consistently improves registration accuracy, with the error-gated variant reducing MSE by 27‑37% on pretrained models and lowering ejection fraction estimation errors.
The paper introduces a new framework for learning continuous-time diffeomorphic image registration by modeling a non-autonomous ODE as a two-parameter flow map. By enforcing cocycle consistency, the method learns flow maps without time discretization or velocity integration during training, enabling efficient inference with few compositions. Experiments on nine datasets show consistent alignment improvements, including a 2.1% Dice gain on brain MRI, 12% TRE reduction on lung CT, and 2.6% Dice improvement on cardiac MRI and ultrasound.
arXiv:2606. 07257v1 Announce Type: cross Abstract: Phase retrieval - recovering a complex-valued field from intensity measurements - is typically solved using variants of the Gerchberg-Saxton (GS) algorithm, understood as alternating projections between measurement planes.
arXiv:2609. 17298v1 Announce Type: cross Abstract: This work introduces quantum-inspired tensor-network circuits as trainable transforms for image inpainting.
The paper evaluates hybrid quantum‑classical machine learning for predicting reduced‑order spatiotemporal brain deformation fields. Using Proper Orthogonal Decomposition to compress high‑dimensional displacement data, the authors compare static temporal‑to‑latent regression and autoregressive latent forecasting models. Classical neural networks outperform all quantum variants, though enhanced quantum circuits improve over minimal ones, indicating that classical architectures still hold a clear advantage in fidelity and stability for this task.