arXiv subjects

Till Nicke

Publications and source records attributed to Till Nicke.

4 recordsLinked to original sources

arXiv2026

CoM$^3$eT: A foundation model for medical image analysis through federated, multidimensional context integration

Medical foundation models improve generalization when training AI models with limited labeled data, but remain confined to a single specialty, such as pathology or radiology, and to either sparse or dense outputs, such as classification or segmentation. Here, we present CoM$^3$eT (Co-representation Multidimensional Multitask Medical Transformer), a medical vision foundation model that unifies pathology and radiology, sparse and dense predictions, and two- and higher-dimensional inputs by modeling multidimensional context with attention. CoM$^3$eT outperformed other medical foundation models in an open competition spanning five tomographic, four whole-specimen, and three two-dimensional datasets, covering sparse and dense prediction tasks as well as report generation. When adapted across diverse clinical applications, training fewer than 2.5% of parameters achieved performance comparable to full fine-tuning, enabling research without access to high-performance GPU clusters. Applied to federated learning across hospitals, this approach achieved performance comparable to pooled-data training over internet connections and with consumer-grade hardware.

cs.CV↗

arXiv2025

Whole Slide Concepts: A Supervised Foundation Model For Pathological Images

Foundation models (FMs) are transforming computational pathology by offering new ways to analyze histopathology images. However, FMs typically require weeks of training on large databases, making their creation a resource-intensive process. In this paper, we present a training for foundation models from whole slide images using supervised, end-to-end, multitask learning on slide-level labels. Notably, it is the first model to incorporate cancer subtyping, risk estimation, and genetic mutation prediction into one model. The presented model outperforms self-supervised models on seven benchmark tasks while the training only required 5% of the computational resources. The results not only show that supervised training can outperform self-supervision with less data, but also offer a solution to annotation problems, as patient-based labels are widely available through routine clinical processes. Furthermore, an attention module provides a layer of explainability across different tasks and serves as a tumor detector for unseen cancer types. To address the issue of closed-source datasets, the model was fully trained on openly available data. The code and model weights are made available under https://github.com/FraunhoferMEVIS/MedicalMultitaskModeling.

eess.IV↗

arXiv2024

Tissue Concepts: supervised foundation models in computational pathology

Due to the increasing workload of pathologists, the need for automation to support diagnostic tasks and quantitative biomarker evaluation is becoming more and more apparent. Foundation models have the potential to improve generalizability within and across centers and serve as starting points for data efficient development of specialized yet robust AI models. However, the training foundation models themselves is usually very expensive in terms of data, computation, and time. This paper proposes a supervised training method that drastically reduces these expenses. The proposed method is based on multi-task learning to train a joint encoder, by combining 16 different classification, segmentation, and detection tasks on a total of 912,000 patches. Since the encoder is capable of capturing the properties of the samples, we term it the Tissue Concepts encoder. To evaluate the performance and generalizability of the Tissue Concepts encoder across centers, classification of whole slide images from four of the most prevalent solid cancers - breast, colon, lung, and prostate - was used. The experiments show that the Tissue Concepts model achieve comparable performance to models trained with self-supervision, while requiring only 6% of the amount of training patches. Furthermore, the Tissue Concepts encoder outperforms an ImageNet pre-trained encoder on both in-domain and out-of-domain data.

eess.IV↗

arXiv2023

Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model

Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more heterogeneous datasets common in biomedical imaging. Here, we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a Universal bioMedical PreTrained model (UMedPT) on a multi-task database including tomographic, microscopic, and X-ray images, with various labelling strategies such as classification, segmentation, and object detection. The UMedPT foundational model outperformed ImageNet pretraining and the previous state-of-the-art models. For tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required not more than 50% of the original training data. In an external independent validation imaging features extracted using UMedPT proved to be a new standard for cross-center transferability.

cs.CV↗