Gonzalo R. Ríos-Muñoz

Roman Kinakh, Gonzalo R. Ríos-Muñoz, Arrate Muñoz-Barrutia

Accurate assessment of PD-L1 expression is critical for guiding immunotherapy, yet current immunohistochemistry (IHC) based methods are resource-intensive. We present nnUNet-B: a Bayesian segmentation framework that infers PD-L1 expression directly from H&E-stained histology images using Multimodal Posterior Sampling (MPS). Built upon nnUNet-v2, our method samples diverse model checkpoints during cyclic training to approximate the posterior, enabling both accurate segmentation and epistemic uncertainty estimation via entropy and standard deviation. Evaluated on a dataset of lung squamous cell carcinoma, our approach achieves competitive performance against established baselines with mean Dice Score and mean IoU of 0.805 and 0.709, respectively, while providing pixel-wise uncertainty maps. Uncertainty estimates show strong correlation with segmentation error, though calibration remains imperfect. These results suggest that uncertainty-aware H&E-based PD-L1 prediction is a promising step toward scalable, interpretable biomarker assessment in clinical workflows.

Long Lin, Pablo Peiro-Corbacho, Pablo Ávila et al.

Intracavitary atrial electrograms (EGMs) provide high-resolution insights into cardiac electrophysiology but are often contaminated by noise and remain high-dimensional, limiting real-time analysis. We introduce CLARAE (CLArity-preserving Reconstruction AutoEncoder), a one-dimensional encoder--decoder designed for atrial EGMs, which achieves both high-fidelity reconstruction and a compact 64-dimensional latent representation. CLARAE is designed to preserve waveform morphology, mitigate reconstruction artifacts, and produce interpretable embeddings through three principles: downsampling with pooling, a hybrid interpolation--convolution upsampling path, and a bounded latent space. We evaluated CLARAE on 495,731 EGM segments (unipolar and bipolar) from 29 patients across three rhythm types (AF, SR300, SR600). Performance was benchmarked against six state-of-the-art autoencoders using reconstruction metrics, rhythm classification, and robustness across signal-to-noise ratios from -5 to 15 dB. In downstream rhythm classification, CLARAE achieved F1-scores above 0.97 for all rhythm types, and its latent space showed clear clustering by rhythm. In denoising tasks, it consistently ranked among the top performers for both unipolar and bipolar signals. In order to promote reproducibility and enhance accessibility, we offer an interactive web-based application. This platform enables users to explore pre-trained CLARAE models, visualize the reconstructions, and compute metrics in real time. Overall, CLARAE combines robust denoising with compact, discriminative representations, offering a practical foundation for clinical workflows such as rhythm discrimination, signal quality assessment, and real-time mapping.

Pablo Peiro-Corbacho, Long Lin, Pablo Ávila et al.

Atrial Fibrillation (AF) is the most prevalent sustained arrhythmia, yet current ablation therapies, including pulmonary vein isolation, are frequently ineffective in persistent AF due to the involvement of non-pulmonary vein drivers. This study proposes a deep learning framework using convolutional autoencoders for unsupervised feature extraction from unipolar and bipolar intracavitary electrograms (EGMs) recorded during AF in ablation studies. These latent representations of atrial electrical activity enable the characterization and automation of EGM analysis, facilitating the detection of AF drivers. The database consisted of 11,404 acquisitions recorded from 291 patients, containing 228,080 unipolar EGMs and 171,060 bipolar EGMs. The autoencoders successfully learned latent representations with low reconstruction loss, preserving the morphological features. The extracted embeddings allowed downstream classifiers to detect rotational and focal activity with moderate performance (AUC 0.73-0.76) and achieved high discriminative performance in identifying atrial EGM entanglement (AUC 0.93). The proposed method can operate in real-time and enables integration into clinical electroanatomical mapping systems to assist in identifying arrhythmogenic regions during ablation procedures. This work highlights the potential of unsupervised learning to uncover physiologically meaningful features from intracardiac signals.