AI-based analysis of histopathology whole slide images (WSIs) is central in
computational pathology. However, image quality can impact model performance.
Here, we investigate to what extent unsharp areas of WSIs impact deep
convolutional neural network classification performance. We propose a
multi-model approach, i.e. DeepBlurMM, to alleviate the impact of unsharp image
areas and improve the model performance. DeepBlurMM uses the sigma cut-offs to
determine the most suitable model for predicting tiles with various levels of
blurring within a single WSI, where sigma is the standard deviation of the
Gaussian distribution. Specifically, the cut-offs categorise the tiles into
sharp or slight blur, moderate blur, and high blur. Each blur level has a
corresponding model to be selected for tile-level predictions. Throughout the
simulation study, we demonstrated the application of DeepBlurMM in a binary
classification task for breast cancer Nottingham Histological Grade 1 vs 3.
Performance, evaluated over 5-fold cross-validation, showed that DeepBlurMM
outperformed the base model under moderate blur and mixed blur conditions.
Unsharp image tiles (local blurriness) at prediction time reduced model
performance. The proposed multi-model approach improved performance under some
conditions, with the potential to improve quality in both research and clinical
applications.

通过 DeepBlurMM 多模型方法，通过识别 WSIs 中不同程度的模糊区域，提高了深度卷积神经网络分类性能。在乳腺癌 Nottingham 组织学分级 1 和 3 的二分类任务中，DeepBlurMM 模型在中度模糊和混合模糊条件下表现优于基准模型，该方法具有改善研究和临床应用质量的潜力。

深模糊多模型（DeepBlurMM）-- 在组织病理学图像分析中减轻图像模糊对深度学习模型性能的影响的策略

Deep Blur Multi-Model (DeepBlurMM) -- a strategy to mitigate the impact  of image blur on deep learning model performance in histopathology image  analysis

Whole Slide Images (WSI), obtained by high-resolution digital scanning of
microscope slides at multiple scales, are the cornerstone of modern Digital
Pathology. However, they represent a particular challenge to
AI-based/AI-mediated analysis because pathology labeling is typically done at
slide-level, instead of tile-level. It is not just that medical diagnostics is
recorded at the specimen level, the detection of oncogene mutation is also
experimentally obtained, and recorded by initiatives like The Cancer Genome
Atlas (TCGA), at the slide level. This configures a dual challenge: a)
accurately predicting the overall cancer phenotype and b) finding out what
cellular morphologies are associated with it at the tile level. To address
these challenges, a weakly supervised Multiple Instance Learning (MIL) approach
was explored for two prevalent cancer types, Invasive Breast Carcinoma
(TCGA-BRCA) and Lung Squamous Cell Carcinoma (TCGA-LUSC). This approach was
explored for tumor detection at low magnification levels and TP53 mutations at
various levels. Our results show that a novel additive implementation of MIL
matched the performance of reference implementation (AUC 0.96), and was only
slightly outperformed by Attention MIL (AUC 0.97). More interestingly from the
perspective of the molecular pathologist, these different AI architectures
identify distinct sensitivities to morphological features (through the
detection of Regions of Interest, RoI) at different amplification levels.
Tellingly, TP53 mutation was most sensitive to features at the higher
applications where cellular morphology is resolved.

通过弱监督的多实例学习方法，利用全幅显微镜图像整体癌症表型的预测来探索肿瘤检测和癌基因突变检测，结果显示与参考实现（AUC 0.96）相匹配的新型叠加多实例学习和关注多实例学习（AUC 0.97）对肿瘤和 TP53 突变的检测能力优于其他 AI 架构，并在不同放大级别下显示对形态特征的不同敏感性。