Institution profile

Charité - Universitätsmedizin Berlin

Academic institutioneurope · de
Official website
Research library12linked papers
Opportunities0open roles
Selected work

Representative Papers

CoM$^3$eT: A foundation model for medical image analysis through federated, multidimensional context integration

Aug 17, 2026

This study addresses the fragmentation of specialties and limited modality support in existing medical foundation models by proposing a unified visual foundation model integrating pathology and radiology. Leveraging a multi-dimensional context attention mechanism, the framework unifies sparse and dense prediction tasks across 2D and high-dimensional inputs. Combined with multi-task joint training and parameter-efficient fine-tuning (PEFT), it enables federated learning on consumer-grade hardware. This work represents the first cross-specialty, multi-dimensional unified modeling approach for medical vision, achieving state-of-the-art performance across 12 benchmark datasets. Notably, fine-tuning less than 2.5% of parameters yields results comparable to full fine-tuning, while federated learning performance closely approximates centralized training, significantly enhancing adaptation efficiency in low-resource settings.

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An Event-Driven Cloud-Native Wearable Analytics Framework for Real-Time Clinical Workloads

Aug 11, 2026

This work addresses the integration challenges posed by consumer-grade wearable devices in clinical settings—stemming from device heterogeneity, proprietary data formats, and regulatory compliance requirements—by proposing an event-driven, cloud-native high-throughput system. The system leverages a cross-platform mobile application to collect high-frequency physiological data and employs a microservices architecture coupled with a stream processing engine to enable FHIR-compliant data standardization, real-time analytics, and end-to-end machine learning support. It introduces a novel dependency-aware FHIR minimization strategy that significantly reduces storage overhead while preserving lossless data reconstruction, thereby establishing a vendor-agnostic, scalable clinical integration framework. Evaluated performance demonstrates support for up to 50 ingestion requests per second with a median response latency under 8 milliseconds, satisfying stringent low-latency monitoring demands while adhering to healthcare regulatory standards.

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A Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies

Aug 06, 2026

Existing methods struggle to consistently define and accurately evaluate the separation between aleatoric and epistemic uncertainty, often relying on imperfect proxy tasks due to the absence of ground-truth uncertainty targets. This work proposes a unified definition of uncertainty as the pointwise posterior risk—the expected loss of a predictor with respect to the true function distribution given observed data—thereby integrating Bayesian functional uncertainty with estimation bias. Building on this formulation, we introduce the first semi-synthetic benchmark that provides direct access to ground-truth uncertainty targets, eliminating dependence on proxy tasks. Experiments reveal that predictive accuracy does not necessarily correlate with uncertainty reliability, enabling clear identification of methods aligned with true uncertainty while exposing their sensitivity to data and modeling choices.

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Recent publications

Latest Papers

CoM$^3$eT: A foundation model for medical image analysis through federated, multidimensional context integration

Aug 17, 2026

This study addresses the fragmentation of specialties and limited modality support in existing medical foundation models by proposing a unified visual foundation model integrating pathology and radiology. Leveraging a multi-dimensional context attention mechanism, the framework unifies sparse and dense prediction tasks across 2D and high-dimensional inputs. Combined with multi-task joint training and parameter-efficient fine-tuning (PEFT), it enables federated learning on consumer-grade hardware. This work represents the first cross-specialty, multi-dimensional unified modeling approach for medical vision, achieving state-of-the-art performance across 12 benchmark datasets. Notably, fine-tuning less than 2.5% of parameters yields results comparable to full fine-tuning, while federated learning performance closely approximates centralized training, significantly enhancing adaptation efficiency in low-resource settings.

0 citationsRead paper

An Event-Driven Cloud-Native Wearable Analytics Framework for Real-Time Clinical Workloads

Aug 11, 2026

This work addresses the integration challenges posed by consumer-grade wearable devices in clinical settings—stemming from device heterogeneity, proprietary data formats, and regulatory compliance requirements—by proposing an event-driven, cloud-native high-throughput system. The system leverages a cross-platform mobile application to collect high-frequency physiological data and employs a microservices architecture coupled with a stream processing engine to enable FHIR-compliant data standardization, real-time analytics, and end-to-end machine learning support. It introduces a novel dependency-aware FHIR minimization strategy that significantly reduces storage overhead while preserving lossless data reconstruction, thereby establishing a vendor-agnostic, scalable clinical integration framework. Evaluated performance demonstrates support for up to 50 ingestion requests per second with a median response latency under 8 milliseconds, satisfying stringent low-latency monitoring demands while adhering to healthcare regulatory standards.

0 citationsRead paper

A Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies

Aug 06, 2026

Existing methods struggle to consistently define and accurately evaluate the separation between aleatoric and epistemic uncertainty, often relying on imperfect proxy tasks due to the absence of ground-truth uncertainty targets. This work proposes a unified definition of uncertainty as the pointwise posterior risk—the expected loss of a predictor with respect to the true function distribution given observed data—thereby integrating Bayesian functional uncertainty with estimation bias. Building on this formulation, we introduce the first semi-synthetic benchmark that provides direct access to ground-truth uncertainty targets, eliminating dependence on proxy tasks. Experiments reveal that predictive accuracy does not necessarily correlate with uncertainty reliability, enabling clear identification of methods aligned with true uncertainty while exposing their sensitivity to data and modeling choices.

0 citationsRead paper