arXiv:2510.27326v1 Announce Type: new 
Abstract: The ODELIA Breast MRI Challenge 2025 addresses a critical issue in breast cancer screening: improving early detection through more efficient and accurate interpretation of breast MRI scans. Even though methods for general-purpose whole-body lesion segmentation as well as multi-time-point analysis exist, breast cancer detection remains highly challenging, largely due to the limited availability of high-quality segmentation labels. Therefore, developing robust classification-based approaches is crucial for the future of early breast cancer detection, particularly in applications such as large-scale screening. In this write-up, we provide a comprehensive overview of our approach to the challenge. We begin by detailing the underlying concept and foundational assumptions that guided our work. We then describe the iterative development process, highlighting the key stages of experimentation, evaluation, and refinement that shaped the evolution of our solution. Finally, we present the reasoning and evidence that informed the design choices behind our final submission, with a focus on performance, robustness, and clinical relevance. We release our full implementation publicly at https://github.com/MIC-DKFZ/MeisenMeister

تحدي ODELIA لصور الرنين المغناطيسي للثدي 2025 يهدف إلى تحسين الكشف المبكر عن سرطان الثدي من خلال التركيز على التفسير الفعال والدقيق لصور الرنين المغناطيسي. هذه المبادرة مهمة لأنها تتناول التحديات المستمرة في فحص سرطان الثدي، وخاصة ندرة تسميات التقسيم عالية الجودة. من خلال تحسين طرق التصنيف، يسعى التحدي إلى تمهيد الطريق لأدوات تشخيصية أفضل، مما يؤدي في النهاية إلى تدخلات مبكرة ونتائج أفضل للمرضى.

El Desafío ODELIA Breast MRI 2025 tiene como objetivo mejorar la detección temprana del cáncer de mama al centrarse en la interpretación eficiente y precisa de las resonancias magnéticas. Esta iniciativa es crucial, ya que aborda los desafíos persistentes en la detección del cáncer de mama, especialmente la escasez de etiquetas de segmentación de alta calidad. Al mejorar los métodos de clasificación, el desafío busca allanar el camino para mejores herramientas de diagnóstico, lo que en última instancia conducirá a intervenciones más tempranas y mejores resultados para los pacientes.

Le défi ODELIA Breast MRI 2025 vise à améliorer la détection précoce du cancer du sein en se concentrant sur l'interprétation efficace et précise des IRM mammaires. Cette initiative est cruciale car elle aborde les défis persistants dans le dépistage du cancer du sein, en particulier la rareté des étiquettes de segmentation de haute qualité. En améliorant les méthodes de classification, le défi vise à ouvrir la voie à de meilleurs outils de diagnostic, conduisant finalement à des interventions plus précoces et à de meilleurs résultats pour les patients.

The ODELIA Breast MRI Challenge 2025 is set to enhance early breast cancer detection by focusing on the efficient and accurate interpretation of MRI scans. This initiative is crucial as it addresses the ongoing challenges in breast cancer screening, particularly the scarcity of high-quality segmentation labels. By improving classification methods, the challenge aims to pave the way for better diagnostic tools, ultimately leading to earlier interventions and improved patient outcomes.

MeisenMeister: A Simple Two Stage Pipeline for Breast Cancer Classification on MRI

arXiv:2601.08336v1 Announce Type: new 
Abstract: Automatic integration of whole slide images (WSIs) and gene expression profiles has demonstrated substantial potential in precision clinical diagnosis and cancer progression studies. However, most existing studies focus on individual gene sequences and slide level classification tasks, with limited attention to spatial transcriptomics and patch level applications. To address this limitation, we propose a multimodal network, BioMorphNet, which automatically integrates tissue morphological features and spatial gene expression to support tissue classification and differential gene analysis. For considering morphological features, BioMorphNet constructs a graph to model the relationships between target patches and their neighbors, and adjusts the response strength based on morphological and molecular level similarity, to better characterize the tumor microenvironment. In terms of multimodal interactions, BioMorphNet derives clinical pathway features from spatial transcriptomic data based on a predefined pathway database, serving as a bridge between tissue morphology and gene expression. In addition, a novel learnable pathway module is designed to automatically simulate the biological pathway formation process, providing a complementary representation to existing clinical pathways. Compared with the latest morphology gene multimodal methods, BioMorphNet's average classification metrics improve by 2.67%, 5.48%, and 6.29% for prostate cancer, colorectal cancer, and breast cancer datasets, respectively. BioMorphNet not only classifies tissue categories within WSIs accurately to support tumor localization, but also analyzes differential gene expression between tissue categories based on prediction confidence, contributing to the discovery of potential tumor biomarkers.

تم اقتراح شبكة متعددة الوسائط جديدة تُدعى BioMorphNet لتحسين تصنيف الأنسجة وتحليل صور الشرائح الكاملة من خلال دمج الميزات الشكلية والتعبير الجيني المكاني، مما يعالج القيود في منهجيات البحث الحالية حول السرطان.

Se ha propuesto una nueva red multimodal llamada BioMorphNet para mejorar la clasificación de tejidos y el análisis de imágenes de diapositivas completas mediante la integración de características morfológicas y expresión génica espacial, abordando las limitaciones de las metodologías actuales en la investigación del cáncer.

Un nouveau réseau multimodal nommé BioMorphNet a été proposé pour améliorer la classification des tissus et l'analyse des images à grande échelle en intégrant des caractéristiques morphologiques et l'expression génique spatiale, répondant aux limitations des méthodologies de recherche sur le cancer actuelles.

A new multimodal network named BioMorphNet has been proposed to enhance tissue classification and whole-slide image analysis by integrating morphological features and spatial gene expression, addressing limitations in current cancer research methodologies.

Tissue Classification and Whole-Slide Images Analysis via Modeling of the Tumor Microenvironment and Biological Pathways

One More Thing in AI – Your Shortcut to AI Mastery

MeisenMeister: A Simple Two Stage Pipeline for Breast Cancer Classification on MRI

Was this article worth reading? Share it

One More Thing in AI

LucidQuery AI

Attentive AI

Meteoria

FastML

Twofold Health

Ready to build your own newsroom?