Lennart Maack

Orcid: 0009-0002-0097-0157

According to our database1, Lennart Maack authored at least 12 papers between 2024 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
An Approach to Enriching Surgical Video Datasets for Fine-Grained Spatial-Temporal Understanding of Vision-Language Models.
CoRR, April, 2026

Surgical instrument-tissue interaction recognition with multi-task-attention video transformer.
Int. J. Comput. Assist. Radiol. Surg., January, 2026

A review of deep learning-based Unsupervised Anomaly Detection in brain MRI.
Medical Image Anal., 2026

Distilling Expert Surgical Knowledge: How to Train Local Surgical VLMs for Anatomy Explanation in Complete Mesocolic Excision.
Proceedings of the 23rd IEEE International Symposium on Biomedical Imaging, 2026

2025
Tracking Any Point Methods for Markerless 3D Tissue Tracking in Endoscopic Stereo Images.
CoRR, August, 2025

Guided reconstruction with conditioned diffusion models for unsupervised anomaly detection in brain MRIs.
Comput. Biol. Medicine, 2025

2024
PolypNextLSTM: a lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM.
Int. J. Comput. Assist. Radiol. Surg., October, 2024

Self-supervised learning for classifying paranasal anomalies in the maxillary sinus.
Int. J. Comput. Assist. Radiol. Surg., September, 2024

Efficient Anatomy Segmentation in Laparoscopic Surgery using Multi-Teacher Knowledge Distillation.
Proceedings of the Medical Imaging with Deep Learning, 3-5 July 2024, Paris, France., 2024

Combining Reconstruction-based Unsupervised Anomaly Detection with Supervised Segmentation for Brain MRIs.
Proceedings of the Medical Imaging with Deep Learning, 3-5 July 2024, Paris, France., 2024

Leveraging the Mahalanobis Distance to Enhance Unsupervised Brain MRI Anomaly Detection.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024


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