arXiv:2510.24473v1 Announce Type: new 
Abstract: This study presents a comparative methodological analysis of six machine learning models for survival analysis (MLSA). Using data from nearly 45,000 colorectal cancer patients in the Hospital-Based Cancer Registries of S\~ao Paulo, we evaluated Random Survival Forest (RSF), Gradient Boosting for Survival Analysis (GBSA), Survival SVM (SSVM), XGBoost-Cox (XGB-Cox), XGBoost-AFT (XGB-AFT), and LightGBM (LGBM), capable of predicting survival considering censored data. Hyperparameter optimization was performed with different samplers, and model performance was assessed using the Concordance Index (C-Index), C-Index IPCW, time-dependent AUC, and Integrated Brier Score (IBS). Survival curves produced by the models were compared with predictions from classification algorithms, and predictor interpretation was conducted using SHAP and permutation importance. XGB-AFT achieved the best performance (C-Index = 0.7618; IPCW = 0.7532), followed by GBSA and RSF. The results highlight the potential and applicability of MLSA to improve survival prediction and support decision making.

أحرزت دراسة حديثة تقدمًا كبيرًا في تحليل البقاء من خلال مقارنة ستة نماذج من التعلم الآلي باستخدام بيانات من حوالي 45,000 مريض بسرطان القولون والمستقيم في ساو باولو. تسلط هذه الأبحاث الضوء على فعالية نماذج مثل Random Survival Forest وGradient Boosting for Survival Analysis، مما يوفر رؤى قيمة يمكن أن تعزز دقة التنبؤ في علاج السرطان. تعتبر هذه التطورات حاسمة لأنها يمكن أن تؤدي إلى نتائج أفضل للمرضى واستراتيجيات رعاية صحية أكثر تخصيصًا.

Un estudio reciente ha logrado avances significativos en el análisis de supervivencia al comparar seis modelos de aprendizaje automático utilizando datos de casi 45,000 pacientes con cáncer colorrectal en São Paulo. Esta investigación destaca la efectividad de modelos como Random Survival Forest y Gradient Boosting for Survival Analysis, proporcionando información valiosa que podría mejorar la precisión predictiva en el tratamiento del cáncer. Tales avances son cruciales, ya que pueden llevar a mejores resultados para los pacientes y a estrategias de atención médica más personalizadas.

Une étude récente a fait des progrès significatifs dans l'analyse de survie en comparant six modèles d'apprentissage automatique à l'aide de données provenant de près de 45 000 patients atteints de cancer colorectal à São Paulo. Cette recherche met en lumière l'efficacité de modèles tels que Random Survival Forest et Gradient Boosting for Survival Analysis, offrant des perspectives précieuses qui pourraient améliorer la précision prédictive dans le traitement du cancer. De tels progrès sont cruciaux car ils peuvent conduire à de meilleurs résultats pour les patients et à des stratégies de soins de santé plus personnalisées.

A recent study has made significant strides in survival analysis by comparing six machine learning models using data from nearly 45,000 colorectal cancer patients in São Paulo. This research highlights the effectiveness of models like Random Survival Forest and Gradient Boosting for Survival Analysis, providing valuable insights that could enhance predictive accuracy in cancer treatment. Such advancements are crucial as they can lead to better patient outcomes and more personalized healthcare strategies.

Methodology for Comparing Machine Learning Algorithms for Survival Analysis

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.

Methodology for Comparing Machine Learning Algorithms for Survival Analysis

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