arXiv Machine Learning
Neural Supervised Clustering for Learning Individualized Treatment Rules with Many Treatments
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
Hugging Face Trending Papers
Jul 21
Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.
arXiv AI
Oct 5
DuetMoE: Coupling Inter- and Intra-Subgroup Robustness for Fair Medical Image Analysis
arXiv:2605.10521v2 Announce Type: replace-cross Abstract: As medical AI expands across diverse healthcare settings worldwide, equitable performance across patient populations is becoming essential to...
arXiv Machine Learning
Aug 24
PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction
PerturbRx is a treatment‑conditioned representation learning framework that learns latent transitions induced by drug interventions. It trains a drug‑ and dose‑conditioned transition predictor using control and treated single‑cell populations, then applies this predictor to pretreatment patient profiles to generate response features without needing post‑treatment data. On TCGA and patient‑derived xenograft benchmarks, PerturbRx outperforms other methods, demonstrating the value of perturbation‑pretrained latent transitions for patient‑level drug‑response prediction.
arXiv Machine Learning
Jun 10
Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering
arXiv:2604. 07085v2 Announce Type: replace Abstract: In electronic health records (EHRs), clustering patients and distinguishing disease subtypes are key tasks to elucidate pathophysiology and aid clinical decision-making.
arXiv Machine Learning
Jul 30
Challenges and proposed solutions in modeling multimodal medical data: A systematic review
arXiv:2505. 06945v5 Announce Type: replace Abstract: Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records.
arXiv Computer Vision
Aug 21
MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities
arXiv:2608. 19788v1 Announce Type: cross Abstract: Trustworthy multimodal fusion in clinical settings requires handling incomplete and heterogeneous modality subsets across institutions, where privacy constraints prohibit centralized data sharing.