21 citations · 35 across the 9 of their papers we have counts for
10 papers
An Approach to Enriching Surgical Video Datasets for Fine-Grained Spatial-Temporal Understanding of Vision-Language Models
Lennart Maack, Alexander Schlaefer
Surgical video understanding is a crucial prerequisite for advancing Computer-Assisted Surgery. While vision-language models (VLMs) have recently been applied to the surgical domai…
Distilling Expert Surgical Knowledge: How to train local surgical VLMs for anatomy explanation in Complete Mesocolic Excision
Lennart Maack, Julia-Kristin Graß, Lisa-Marie Toscha +2
Recently, Vision Large Language Models (VLMs) have demonstrated high potential in computer-aided diagnosis and decision-support. However, current VLMs show deficits in domain speci…
Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection
Finn Behrendt, Debayan Bhattacharya, Robin Mieling +4
Unsupervised Anomaly Detection (UAD) methods rely on healthy data distributions to identify anomalies as outliers. In brain MRI, a common approach is reconstruction-based UAD, wher…
Self-supervised learning for classifying paranasal anomalies in the maxillary sinus
Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker +9
Purpose: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised…
Sliced Online Model Checking for Optimizing the Beam Scheduling Problem in Robotic Radiation Therapy
Lars Beckers, Stefan Gerlach, Ole Lübke +2
In robotic radiation therapy, high-energy photon beams from different directions are directed at a target within the patient. Target motion can be tracked by robotic ultrasound and…
Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection
Finn Behrendt, Debayan Bhattacharya, Lennart Maack +4
Supervised deep learning techniques show promise in medical image analysis. However, they require comprehensive annotated data sets, which poses challenges, particularly for rare d…
PolypNextLSTM: A lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM
Debayan Bhattacharya, Konrad Reuter, Finn Behrendt +3
Commonly employed in polyp segmentation, single image UNet architectures lack the temporal insight clinicians gain from video data in diagnosing polyps. To mirror clinical practice…
Nodule detection and generation on chest X-rays: NODE21 Challenge
Ecem Sogancioglu, Bram van Ginneken, Finn Behrendt +16
Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep lea…
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs
Finn Behrendt, Debayan Bhattacharya, Robin Mieling +4
The application of supervised models to clinical screening tasks is challenging due to the need for annotated data for each considered pathology. Unsupervised Anomaly Detection (UA…
Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods
Nils Gessert, Matthias Schlüter, Sarah Latus +3
Early detection of cancer is crucial for treatment and overall patient survival. In the upper aerodigestive tract (UADT) the gold standard for identification of malignant tissue is…