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20212025
most citedUnderstanding metric-related pitfalls in image analysis validation

204 citations · 381 across the 42 of their papers we have counts for

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Showing 2024Show all

12 papers · 1 filter

cs.CV2024

Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation

André Ferreira, Tiago Jesus, Behrus Puladi +3

This paper presents the winning solution of task 1 and the third-placed solution of task 3 of the BraTS challenge. The use of automated tools in clinical practice has increased due…

cs.CV2024

Brain Tumour Removing and Missing Modality Generation using 3D WDM

André Ferreira, Gijs Luijten, Behrus Puladi +3

This paper presents the second-placed solution for task 8 and the participation solution for task 7 of BraTS 2024. The adoption of automated brain analysis algorithms to support cl…

eess.IV2024

Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy

Nikoo Moradi, André Ferreira, Behrus Puladi +5

Radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional ima…

cs.CL2024★ 8 cited

Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data

Felix J. Dorfner, Amin Dada, Felix Busch +8

Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data. However, the effectiveness of this appro…

cs.CV2024

Tumor likelihood estimation on MRI prostate data by utilizing k-Space information

M. Rempe, F. Hörst, C. Seibold +7

We present a novel preprocessing and prediction pipeline for the classification of magnetic resonance imaging (MRI) that takes advantage of the information rich complex valued k-Sp…

cs.CL2024★ 3 cited

Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding

Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer +17

Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate…

cs.CV2024

Deep Learning-based Point Cloud Registration for Augmented Reality-guided Surgery

Maximilian Weber, Daniel Wild, Jens Kleesiek +2

Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision, with applications in areas such as augmented reality (AR)…

cs.CL2024★ 3 cited

Does Biomedical Training Lead to Better Medical Performance?

Amin Dada, Marie Bauer, Amanda Butler Contreras +4

Large Language Models (LLMs) are expected to significantly contribute to patient care, diagnostics, and administrative processes. Emerging biomedical LLMs aim to address healthcare…

eess.IV2024

A Semi-automatic Cranial Implant Design Tool Based on Rigid ICP Template Alignment and Voxel Space Reconstruction

Michael Lackner, Behrus Puladi, Jens Kleesiek +2

In traumatic medical emergencies, the patients heavily depend on cranioplasty - the craft of neurocranial repair using cranial implants. Despite the improvements made in recent yea…

eess.IV2024★ 1 cited

Deep PCCT: Photon Counting Computed Tomography Deep Learning Applications Review

Ana Carolina Alves, André Ferreira, Gijs Luijten +4

Medical imaging faces challenges such as limited spatial resolution, interference from electronic noise and poor contrast-to-noise ratios. Photon Counting Computed Tomography (PCCT…

cs.CV2024★ 10 cited

Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images

Johannes Raufeisen, Kunpeng Xie, Fabian Hörst +9

Background: Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine…

eess.IV2024★ 9 cited

How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation

André Ferreira, Naida Solak, Jianning Li +4

Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the me…