Flensburg University of Applied Sciences

Atypical mitosis marks a deviation in the cell division process that has been shown be an independent prognostic marker for tumor malignancy. However, atypical mitosis classification remains challenging due to low prevalence, at times subtle morphological differences from normal mitotic figures, low inter-rater agreement among pathologists, and class imbalance in datasets. Building on the Atypical Mitosis dataset for Breast Cancer (AMi-Br), this study presents a comprehensive benchmark comparing deep learning approaches for automated atypical mitotic figure (AMF) classification, including end-to-end fine-tuned deep learning models, foundation models with linear probing, and foundation models fine-tuned with low-rank adaptation (LoRA). For rigorous evaluation, we further introduce two new held-out AMF datasets - AtNorM-Br, a dataset of mitotic figures from the TCGA breast cancer cohort, and AtNorM-MD, a multi-domain dataset of mitotic figures from a subset of the MIDOG++ training set. We found average balanced accuracy values of up to 0.8135, 0.7788, and 0.7723 on the in-domain AMi-Br and the out-of-domain AtNorm-Br and AtNorM-MD datasets, respectively. Our work shows that atypical mitotic figure classification, while being a challenging problem, can be effectively addressed through the use of recent advances in transfer learning and model fine-tuning techniques. We make all code and data used in this paper available in this github repository: https://github.com/DeepMicroscopy/AMi-Br_Benchmark

Artificial intelligence (AI)-driven decision support systems can improve diagnostic accuracy and efficiency in computational pathology. However, collaboration between human experts and AI may introduce cognitive biases such as automation and anchoring bias, where users adopt system predictions blindly or are disproportionately influenced by AI advice, even when inaccurate. These effects may be amplified under time pressure, common in routine pathology, or shaped by individual user characteristics. We conducted an online experiment in which pathology experts (n = 28) estimated tumor cell percentages: once independently and once with AI support. A subset of estimations in each condition was performed under time strain. Overall, AI assistance improved diagnostic performance but introduced a 7

Object detection is one of the most common tasks in histopathological image analysis and generalization is a key requirement for the clinical applicability of deep object detection models. However, traditional evaluation metrics often fail to provide insights into why models fail on certain test cases, especially in the presence of domain shifts. In this work, we propose a novel quantitative method for assessing the discriminative power of a model’s latent space. Our approach, applicable to all object detection models with known local correspondences such as the popular RetinaNet, FCOS, or YOLO approaches, allows tracing discrimination across layers and coordinates. We motivate, adapt, and evaluate two suitable metrics, the generalized discrimination value and the Hellinger distance, and incorporate them into our approach. Through empirical validation on real-world histopathology datasets, we demonstrate the effectiveness of our method in capturing model discrimination properties and providing insights for architectural optimization. This work contributes to bridging the gap between model performance evaluation and understanding the underlying mechanisms influencing model behavior.

For large-scale floating wind turbines, load and response validation enables a closed-loop design process, which helps to validate the simulation models and algorithms and provides a basis for improving subsequent designs. For this purpose, the characteristics of the freestream inflow wind are vital. The nacelle lidar system is easy to install, cost-effective, and capable of measuring the freestream wind in front of the rotor of a floating turbine. However, compared to bottom-fixed turbines, measurement errors in nacelle lidar caused by the motion of the floating platform require accurate motion measurements and complex reconstruction algorithms for motion compensation. In this paper, we investigate the potential of using nacelle lidar for load and response validation of floating turbines through numerical simulations. We propose a method for generating turbulent wind fields considering rotor-averaged wind speed estimated by simulated-lidar system as a constraint. This method does not require motion velocity measurements and allows for simplified wind field reconstruction. Based on aero-hydro-servo-elastic integrated simulations, this study demonstrates the potential of a nine-beam nacelle lidar system for load validation, including both sample-averaged statistical properties and one-to-one comparisons.

Mitosis detection represents a critical task in digital pathology, as it plays an important role in the tumor grading and prognosis of patients. Manual determination is a labor-intensive task for practitioners with high interobserver variability, thus, automation is a priority. There has been substantial progress towards creating robust mitosis detection algorithms, primarily driven by the Mitosis Domain Generalization (MIDOG) challenges. Also, there has been growing interest in the molecular characterization of mitosis to achieve a more comprehensive understanding of its underlying mechanisms in a subphase-specific manner. We introduce a new mitotic figure dataset annotated with subphase information based on the MIDOG++ dataset as well as a previously unrepresented tumor domain to enhance the diversity and applicability. We envision a new perspective for domain generalization by improving model performance with subtyping mitosis, complemented with an atypical mitotic class. Our work has implications in two main areas: subtyping information can provide helpful information in mitosis detection, while also providing promising new directions in answering biological questions, such as molecular analysis of subphases.