Academic Research
Our work is grounded in peer-reviewed research. Below are selected publications from Dr. Dario Sitnik's academic career in medical imaging, computer vision, and AI.
// Medical Imaging
Deep Learning-Based Mitosis Detection in Breast Cancer Histopathology Images
D. Sitnik, M. Aubreville, A. Maier
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023
We present a novel deep learning approach for automated mitosis detection in H&E-stained breast cancer histopathology slides, achieving state-of-the-art performance on the MIDOG challenge dataset.
Multi-Scale Feature Aggregation for Histopathological Image Classification
D. Sitnik, C. Bertram, R. Klopfleisch, A. Maier
IEEE Transactions on Medical Imaging, 2023
A multi-scale feature aggregation framework for whole-slide image classification that captures both cellular-level and tissue-level morphological patterns in histopathology.
Domain Adaptation for Cross-Scanner Histopathology Image Analysis
D. Sitnik, M. Aubreville, A. Maier
Medical Image Analysis (MedIA), 2022
We address the domain shift problem in computational pathology by proposing a domain adaptation method that generalizes across different scanners and staining protocols.
// Computer Vision
Attention-Guided Object Detection for Industrial Quality Control
D. Sitnik, F. Wagner, A. Maier
European Conference on Computer Vision (ECCV) Workshops, 2022
An attention-guided detection framework optimized for manufacturing defect detection, achieving real-time performance on edge devices while maintaining high accuracy on small defects.
Few-Shot Learning for Visual Inspection in Manufacturing
D. Sitnik, A. Maier
Pattern Recognition (Elsevier), 2022
A few-shot learning approach for visual defect detection that requires only 5-10 labeled examples per defect class, making AI-based quality control practical for small-batch manufacturing.
// Document Intelligence
Layout-Aware Transformer for Structured Document Information Extraction
D. Sitnik, J. Pfeiffer, A. Maier
International Conference on Document Analysis and Recognition (ICDAR), 2023
A layout-aware transformer architecture for extracting structured information from complex documents such as contracts, invoices, and medical records with state-of-the-art accuracy.
Privacy-Preserving Document Processing with Federated Learning
D. Sitnik, A. Maier
ACL Workshop on Privacy in NLP, 2023
A federated learning framework for document processing that enables model training across organizations without sharing sensitive document data, achieving comparable accuracy to centralized approaches.
// Natural Language Processing
Multilingual Legal Document Analysis with Cross-Lingual Transfer
D. Sitnik, A. Maier
Findings of EMNLP, 2023
Cross-lingual transfer learning for legal document analysis across German, English, and Croatian, demonstrating effective knowledge transfer for contract clause classification and risk assessment.
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