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Sergios Gatidis

10 accepted papers

2026

The Loss Is Not Enough: Sampling Conditions and Inductive Bias in Contrastive Representation Learning

ICML 2026poster

Contrastive learning has emerged as a powerful paradigm for self-supervised representation learning, yet the precise conditions under which it recovers meaningful latent structure remain incompletely understood. We develop a measure-theoretic framework that formalizes the diversity condition, a requ…

Cited by 0SourceScholar
2025

Structuring Radiology Reports: Challenging LLMs with Lightweight Models

EMNLP 2025

Radiology reports are critical for clinical decision-making but often lack a standardized format, limiting both human interpretability and machine learning (ML) applications. While large language models (LLMs) have shown strong capabilities in reformatting clinical text, their high computational req

Cited by 0SourcePDFScholar
2024

Deep Regression for Biological Age Estimation in Multiple Organs: Investigations on 40, 000 Subjects of the UK Biobank

ICASSP 2024accepted

Age plays an important role in shaping medical decisions, but the biological changes associated with aging do not solely depend on the chronological age. Genetics, lifestyle, and environment cause variations in age-related characteristics, even within the same chronological age group. Biological age…

Cited by 0SourceScholar
2024

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

AAAI 2024technical

The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care. However, evaluating LLMs on realistic text generation tasks for healthcare remains chall…

Cited by 67SourcePDFScholar
2021

Automated Multi-Organ Segmentation in Pet Images Using Cascaded Training of a 3d U-Net and Convolutional Autoencoder

ICASSP 2021accepted

PET imaging is an important tool in clinical diagnostics, especially in oncology as it is able to visualize ongoing metabolic processes, e.g. caused by a tumor. Due to the low spatial resolution, a corresponding CT or MRI scan is normally necessary to gain knowledge about the physiological structure…

Cited by 0SourceScholar
2021

Uncertainty-Based Biological Age Estimation of Brain MRI Scans

ICASSP 2021accepted

Age is an essential factor in modern diagnostic procedures. However, assessment of the true biological age (BA) remains a daunting task due to the lack of reference ground-truth labels. Current BA estimation approaches are either restricted to skeletal images or rely on non-imaging modalities that y…

Cited by 0SourceScholar
2018

Automated Detection of High FDG Uptake Regions in CT Images

ICASSP 2018accepted

Combined PET-CT scan is an important diagnostic tool in modern medicine, e.g. for staging or treatment planning in the field of oncology. Especially in small structures, like a tumour, textural variations visible in a PET image are not visually recognizable within a CT scan from the same region. Thu…

Cited by 0SourceScholar
2018

Automatic Motion Artifact Detection for Whole-Body Magnetic Resonance Imaging

ICASSP 2018accepted

Magnetic resonance (MR) plays an important role in medical imaging. It can be flexibly tuned towards different applications for deriving a meaningful diagnosis. However, its long acquisition times and flexible parametrization make it on the other hand prone to artifacts which obscure the underlying…

Cited by 0SourceScholar
2016

Active learning for magnetic resonance image quality assessment

ICASSP 2016accepted

In medical imaging, the acquired images are usually analyzed by a human observer and rated with respect to a diagnostic question. However, this procedure is time-demanding and expensive. Further more, the lack of a reference image makes this task challenging. In order to support the human observer i…

Cited by 0SourceScholar