UNICORN: A Deep Learning Model for Integrating Multi-Stain Data in Histopathology

Background: The integration of multi-stain histopathology images through deep learning poses a significant challenge in digital histopathology. Current multi-modal approaches struggle with data heterogeneity and missing data. This study aims to overcome these limitations by developing a novel transformer model for multi-stain integration that can handle missing data during training as well as inference. Methods: We propose UNICORN (UNiversal modality Integration Network for CORonary classificatioN) a multi-modal transformer capable of processing multi-stain histopathology for atherosclerosis severity class prediction. The architecture comprises a two-stage, end-to-end trainable model with specialized modules utilizing transformer self-attention blocks. The initial stage employs domain-specific expert modules to extract features from each modality. In the subsequent stage, an aggregation expert module integrates these features by learning the interactions between the different data modalities. Results: Evaluation was performed using a multi-class dataset of atherosclerotic lesions from the Munich Cardiovascular Studies Biobank (MISSION), using over 4,000 paired multi-stain whole slide images (WSIs) from 170 deceased individuals on 7 prespecified segments of the coronary tree, each stained according to four histopathological protocols. UNICORN achieved a classification accuracy of 0.67, outperforming other state-of-the-art models. The model effectively identifies relevant tissue phenotypes across stainings and implicitly models disease progression. Conclusion: Our proposed multi-modal transformer model addresses key challenges in medical data analysis, including data heterogeneity and missing modalities. Explainability and the model's effectiveness in predicting atherosclerosis progression underscores its potential for broader applications in medical research.

3D Vertebrae Measurements: Assessing Vertebral Dimensions in Human Spine Mesh Models Using Local Anatomical Vertebral Axes

Vertebral morphological measurements are important across various disciplines, including spinal biomechanics and clinical applications, pre- and post-operatively. These measurements also play a crucial role in anthropological longitudinal studies, where spinal metrics are repeatedly documented over extended periods. Traditionally, such measurements have been manually conducted, a process that is time-consuming. In this study, we introduce a novel, fully automated method for measuring vertebral morphology using 3D meshes of lumbar and thoracic spine models.Our experimental results demonstrate the method's capability to accurately measure low-resolution patient-specific vertebral meshes with mean absolute error (MAE) of 1.09 mm and those derived from artificially created lumbar spines, where the average MAE value was 0.7 mm. Our qualitative analysis indicates that measurements obtained using our method on 3D spine models can be accurately reprojected back onto the original medical images if these images are available.

HealthWalk: Promoting Health and Mobility through Sensor-Based Rollator Walker Assistance

Rollator walkers allow people with physical limitations to increase their mobility and give them the confidence and independence to participate in society for longer. However, rollator walker users often have poor posture, leading to further health problems and, in the worst case, falls. Integrating sensors into rollator walker designs can help to address this problem and results in a platform that allows several other interesting use cases. This paper briefly overviews existing systems and the current research directions and challenges in this field. We also present our early HealthWalk rollator walker prototype for data collection with older people, rheumatism, multiple sclerosis and Parkinson patients, and individuals with visual impairments.