arXiv:2502.04946v2 Announce Type: replace 
Abstract: Cancer remains one of the leading causes of morbidity and mortality worldwide. Comprehensive datasets that combine histopathological images with genetic and survival data across various tumour sites are essential for advancing computational pathology and personalised medicine. We present SurGen, a dataset comprising 1,020 H&E-stained whole-slide images (WSIs) from 843 colorectal cancer cases. The dataset includes detailed annotations for key genetic mutations (KRAS, NRAS, BRAF) and mismatch repair status, as well as survival data for 426 cases. We illustrate SurGen's utility with a proof-of-concept model that predicts mismatch repair status directly from WSIs, achieving a test area under the receiver operating characteristic curve of 0.8273. These preliminary results underscore the dataset's potential to facilitate research in biomarker discovery, prognostic modelling, and advanced machine learning applications in colorectal cancer and beyond. SurGen offers a valuable resource for the scientific community, enabling studies that require high-quality WSIs linked with comprehensive clinical and genetic information on colorectal cancer. Our initial findings affirm the dataset's capacity to advance diagnostic precision and foster the development of personalised treatment strategies in colorectal oncology. Data available online: https://doi.org/10.6019/S-BIAD1285.

SurGen هو مجموعة بيانات رائدة تحتوي على 1,020 صورة شريحة كاملة ملونة بصبغة H&E من 843 حالة سرطان قولوني مستعرض، والتي تجمع بين الصور النسيجية الحيوية والبيانات الجينية وبيانات البقاء. تعتبر هذه المورد ضرورية لتقدم علم الأمراض الحاسوبي والطب الشخصي، حيث تتيح للباحثين فهم تقدم السرطان ونتائج العلاج بشكل أفضل. من خلال توفير تعليقات تفصيلية، يعزز SurGen القدرة على تطوير علاجات مستهدفة وتحسين رعاية المرضى، مما يجعله مساهمة مهمة في أبحاث السرطان.

SurGen es un conjunto de datos innovador que presenta 1,020 imágenes de diapositivas completas teñidas con H&E de 843 casos de cáncer colorrectal, combinando imágenes histopatológicas cruciales con datos genéticos y de supervivencia. Este recurso es vital para avanzar en la patología computacional y la medicina personalizada, ya que permite a los investigadores comprender mejor la progresión del cáncer y los resultados del tratamiento. Al proporcionar anotaciones detalladas, SurGen mejora la capacidad de desarrollar terapias dirigidas y mejorar la atención al paciente, convirtiéndose en una contribución significativa a la investigación del cáncer.

SurGen est un ensemble de données révolutionnaire comprenant 1 020 images de lames entières colorées à l'H&E provenant de 843 cas de cancer colorectal, qui combine des images histopathologiques cruciales avec des données génétiques et de survie. Cette ressource est essentielle pour faire progresser la pathologie computationnelle et la médecine personnalisée, car elle permet aux chercheurs de mieux comprendre la progression du cancer et les résultats des traitements. En fournissant des annotations détaillées, SurGen améliore la capacité à développer des thérapies ciblées et à améliorer les soins aux patients, ce qui en fait une contribution significative à la recherche sur le cancer.

SurGen is a groundbreaking dataset featuring 1,020 H&E-stained whole-slide images from 843 colorectal cancer cases, which combines crucial histopathological images with genetic and survival data. This resource is vital for advancing computational pathology and personalized medicine, as it enables researchers to better understand cancer progression and treatment outcomes. By providing detailed annotations, SurGen enhances the ability to develop targeted therapies and improve patient care, making it a significant contribution to cancer research.

SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers

arXiv:2308.00929v3 Announce Type: replace 
Abstract: Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras and plays an important role in the prevention and treatment of colorectal cancer. However, traditional methods for object ReID directly adopting CNN models trained on the ImageNet dataset usually produce unsatisfactory retrieval performance on colonoscopic datasets due to the large domain gap. Additionally, these methods neglect to explore the potential of self-discrepancy among intra-class or inter-class relations in the colonoscopic polyp dataset, which remains an open research problem in the medical community. To solve this dilemma, we propose a simple but effective training method named Colo-ReID, which can help our model learn more general and discriminative knowledge based on the meta-learning strategy in scenarios with fewer samples. Based on this, a dynamic Meta-Learning Regulation mechanism called MLR is introduced to further boost the performance of polyp re-identification. Our experimental results show that Colo-ReID consistently outperforms second-best method in terms of mAP performance by +2.3% on polyp re-identification task. Our source code is also publicly available at https://github.com/JeremyXSC/Colo-ReID.

تم اقتراح طريقة جديدة تُدعى Colo-ReID لإعادة تحديد الهوية للزوائد القولونية، والتي تهدف إلى تحسين مطابقة الزوائد من وجهات نظر مختلفة باستخدام كاميرات متعددة، مما يعالج تحديًا كبيرًا في الوقاية والعلاج من سرطان القولون. لقد واجهت نماذج CNN التقليدية صعوبات بسبب الفجوات في المجال وعدم استكشاف العلاقات داخل الفئة وبين الفئات في مجموعات بيانات الزوائد.

Se ha propuesto un nuevo método llamado Colo-ReID para la re-identificación de pólipos colonoscópicos, que busca mejorar la coincidencia de pólipos desde diversas vistas de cámaras, abordando un desafío significativo en la prevención y tratamiento del cáncer colorrectal. Los modelos CNN tradicionales han tenido dificultades debido a las brechas de dominio y la falta de exploración de las relaciones intra-clase e inter-clase en los conjuntos de datos de pólipos.

Une nouvelle méthode appelée Colo-ReID a été proposée pour la ré-identification des polypes coloscopiques, visant à améliorer l'appariement des polypes à partir de diverses vues de caméras, répondant à un défi majeur dans la prévention et le traitement du cancer colorectal. Les modèles CNN traditionnels ont rencontré des difficultés en raison des écarts de domaine et du manque d'exploration des relations intra-classe et inter-classe dans les ensembles de données de polypes.

A new method called Colo-ReID has been proposed for Colonoscopic Polyp Re-Identification, which aims to enhance the matching of polyps from various camera views, addressing a significant challenge in colorectal cancer prevention and treatment. Traditional CNN models have struggled with this task due to domain gaps and the lack of exploration of intra-class and inter-class relations in polyp datasets.

SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers

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