arXiv:2510.25051v1 Announce Type: new 
Abstract: Breast cancer remains the most commonly diagnosed malignancy among women in the developed world. Early detection through mammography screening plays a pivotal role in reducing mortality rates. While computer-aided diagnosis (CAD) systems have shown promise in assisting radiologists, existing approaches face critical limitations in clinical deployment - particularly in handling the nuanced interpretation of multi-modal data and feasibility due to the requirement of prior clinical history. This study introduces a novel framework that synergistically combines visual features from 2D mammograms with structured textual descriptors derived from easily accessible clinical metadata and synthesized radiological reports through innovative tokenization modules. Our proposed methods in this study demonstrate that strategic integration of convolutional neural networks (ConvNets) with language representations achieves superior performance to vision transformer-based models while handling high-resolution images and enabling practical deployment across diverse populations. By evaluating it on multi-national cohort screening mammograms, our multi-modal approach achieves superior performance in cancer detection and calcification identification compared to unimodal baselines, with particular improvements. The proposed method establishes a new paradigm for developing clinically viable VLM-based CAD systems that effectively leverage imaging data and contextual patient information through effective fusion mechanisms.

تسلط دراسة حديثة الضوء على تطوير نماذج متقدمة للرؤية واللغة (VLM) تهدف إلى تحسين الكشف عن سرطان الثدي من خلال أنظمة التشخيص المدعومة بالكمبيوتر (CAD) المحسنة. هذا مهم لأن سرطان الثدي هو أكثر أنواع السرطان تشخيصًا بين النساء في الدول المتقدمة، والكشف المبكر أمر حاسم لتقليل معدلات الوفيات. تعالج النماذج الجديدة القيود الموجودة في تفسير البيانات المعقدة، مما قد يؤدي إلى نتائج سريرية أفضل واستخدام أكثر فعالية لفحص الماموجرام.

Un estudio reciente destaca el desarrollo de modelos avanzados de visión-lenguaje (VLM) destinados a mejorar la detección del cáncer de mama a través de sistemas de diagnóstico asistido por computadora (CAD) mejorados. Esto es significativo porque el cáncer de mama es el cáncer más comúnmente diagnosticado entre las mujeres en los países desarrollados, y la detección temprana es crucial para reducir las tasas de mortalidad. Los nuevos modelos abordan las limitaciones existentes en la interpretación de datos complejos, lo que podría llevar a mejores resultados clínicos y un uso más efectivo de la mamografía.

Une étude récente met en avant le développement de modèles avancés de vision-langage (VLM) visant à améliorer la détection du cancer du sein grâce à des systèmes de diagnostic assisté par ordinateur (CAD) améliorés. Cela est important car le cancer du sein est le cancer le plus fréquemment diagnostiqué chez les femmes dans les pays développés, et la détection précoce est cruciale pour réduire les taux de mortalité. Les nouveaux modèles répondent aux limitations existantes dans l'interprétation des données complexes, ce qui pourrait conduire à de meilleurs résultats cliniques et à une utilisation plus efficace du dépistage par mammographie.

A recent study highlights the development of advanced vision-language models (VLMs) aimed at improving breast cancer detection through enhanced computer-aided diagnosis (CAD) systems. This is significant because breast cancer is the most commonly diagnosed cancer among women in developed countries, and early detection is crucial for reducing mortality rates. The new models address existing limitations in interpreting complex data, potentially leading to better clinical outcomes and more effective use of mammography screening.

Breast Cancer VLMs: Clinically Practical Vision-Language Train-Inference Models

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

arXiv:2601.08401v1 Announce Type: new 
Abstract: Objectives: To overcome challenges in diagnosing pericoronitis on panoramic radiographs, an AI-assisted assessment system integrating anatomical localization, pathological classification, and interpretability. Methods: A two-stage deep learning pipeline was implemented. The first stage used YOLOv8 to detect third molars and classify their anatomical positions and angulations based on Winter's classification. Detected regions were then fed into a second-stage classifier, a modified ResNet-50 architecture, for detecting radiographic features suggestive of pericoronitis. To enhance clinical trust, Grad-CAM was used to highlight key diagnostic regions on the radiographs. Results: The YOLOv8 component achieved 92% precision and 92.5% mean average precision. The ResNet-50 classifier yielded F1-scores of 88% for normal cases and 86% for pericoronitis. Radiologists reported 84% alignment between Grad-CAM and their diagnostic impressions, supporting the radiographic relevance of the interpretability output. Conclusion: The system shows strong potential for AI-assisted panoramic assessment, with explainable AI features that support clinical confidence.

قدمت دراسة جديدة إطار عمل عميق قابل للتفسير على مرحلتين لتقييم التهاب اللثة المحيطة بالأسنان في الأشعة السينية البانورامية، باستخدام YOLOv8 لتحديد المواقع التشريحية وResNet-50 المعدل لتصنيف الأمراض. حقق النظام دقة عالية وتوافق جيد مع الانطباعات التشخيصية للأطباء، مما يعزز القابلية للتفسير من خلال تصورات Grad-CAM.

Un nuevo estudio ha presentado un marco de aprendizaje profundo explicable en dos etapas para la evaluación de la pericoronitis en radiografías panorámicas, utilizando YOLOv8 para la localización anatómica y un ResNet-50 modificado para la clasificación patológica. El sistema logró una alta precisión y una buena alineación con las impresiones diagnósticas de los radiólogos, mejorando la interpretabilidad a través de visualizaciones Grad-CAM.

Une nouvelle étude a introduit un cadre d'apprentissage profond explicable en deux étapes pour évaluer la péricoronite sur des radiographies panoramiques, utilisant YOLOv8 pour la localisation anatomique et un ResNet-50 modifié pour la classification pathologique. Le système a atteint une haute précision et une bonne concordance avec les impressions diagnostiques des radiologues, améliorant l'interprétabilité grâce aux visualisations Grad-CAM.

A new study has introduced an explainable two-stage deep learning framework for assessing pericoronitis in panoramic radiographs, utilizing YOLOv8 for anatomical localization and a modified ResNet-50 for pathological classification. The system achieved high precision and alignment with radiologists' diagnostic impressions, enhancing interpretability through Grad-CAM visualizations.

An Explainable Two Stage Deep Learning Framework for Pericoronitis Assessment in Panoramic Radiographs Using YOLOv8 and ResNet-50

One More Thing in AI – Your Shortcut to AI Mastery

Breast Cancer VLMs: Clinically Practical Vision-Language Train-Inference Models

Was this article worth reading? Share it

One More Thing in AI

LucidQuery AI

Tattoo Visualizer

OpenL Translator

LangWatch

The Visualizer

Ready to build your own newsroom?