Paper Reading AI Learner

Aggregation Model Hyperparameters Matter in Digital Pathology

2023-11-29 16:54:25
Gustav Bredell, Marcel Fischer, Przemyslaw Szostak, Samaneh Abbasi-Sureshjani, Alvaro Gomariz

Abstract

Digital pathology has significantly advanced disease detection and pathologist efficiency through the analysis of gigapixel whole-slide images (WSI). In this process, WSIs are first divided into patches, for which a feature extractor model is applied to obtain feature vectors, which are subsequently processed by an aggregation model to predict the respective WSI label. With the rapid evolution of representation learning, numerous new feature extractor models, often termed foundational models, have emerged. Traditional evaluation methods, however, rely on fixed aggregation model hyperparameters, a framework we identify as potentially biasing the results. Our study uncovers a co-dependence between feature extractor models and aggregation model hyperparameters, indicating that performance comparability can be skewed based on the chosen hyperparameters. By accounting for this co-dependency, we find that the performance of many current feature extractor models is notably similar. We support this insight by evaluating seven feature extractor models across three different datasets with 162 different aggregation model configurations. This comprehensive approach provides a more nuanced understanding of the relationship between feature extractors and aggregation models, leading to a fairer and more accurate assessment of feature extractor models in digital pathology.

Abstract (translated)

数字病理学通过分析 gigapixel Whole-Slide Images (WSI) 显著提高了疾病检测和病理学家效率。在這個過程中,WSIs 首先被分為斑塊,對其應用一個特徵提取器模型獲得特徵向量,然後由聚合模型進行後續處理以預測相應的 WSI 標籤。隨著表示學習的快速發展,出現了許多新的特徵提取器模型,通常稱為基礎模型。然而,傳統評估方法依賴於固定的聚合模型超參數,這種框架我們認為可能偏颇結果。我們的研究揭示了特徵提取器模型和聚合模型超參數之間的共同依賴關係,表明性能可讀性可能基於選擇的超參數而有所偏差。通過考慮這種共同依賴關係,我們發現許多现有特徵提取器模型的性能非常相似。我們通過在三個不同的數據集上評估七個特徵提取器模型,with 162 different aggregation model configurations,來驗證這個見解。這種全面的方法提供了一個更精確的視角,說明了特徵提取器和聚合模型之間的關係,有助於更公平和準確地評估數字病理學中的特徵提取器模型。

URL

https://arxiv.org/abs/2311.17804

PDF

https://arxiv.org/pdf/2311.17804.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot