Learning suitable Whole slide images (WSIs) representations for efficient retrieval systems is a non-trivial task. The WSI embeddings obtained from current methods are in Euclidean space not ideal for efficient WSI retrieval. Furthermore, most of the current methods require high GPU memory due to the simultaneous processing of multiple sets of patches. To address these challenges, we propose a novel framework for learning binary and sparse WSI representations utilizing a deep generative modelling and the Fisher Vector. We introduce new loss functions for learning sparse and binary permutation-invariant WSI representations that employ instance-based training achieving better memory efficiency. The learned WSI representations are validated on The Cancer Genomic Atlas (TCGA) and Liver-Kidney-Stomach (LKS) datasets. The proposed method outperforms Yottixel (a recent search engine for histopathology images) both in terms of retrieval accuracy and speed. Further, we achieve competitive performance against SOTA on the public benchmark LKS dataset for WSI classification.
翻译:高效检索系统学习适合的全幻灯片图象(WSIs)是一个非三重任务。从当前方法中获得的WSI嵌入空间在欧几里德空间并不理想,无法有效地进行WSI检索。此外,由于同时处理多组补丁,目前大多数方法都需要高GPU内存。为了应对这些挑战,我们提议了一个用于学习二进制和稀疏的WSI图象的新框架,利用深层基因建模和Fisher矢量器。我们引入了学习稀疏和二进制变异WSI图象的新损失功能,采用基于实例的培训来提高记忆效率。所学的WSI表象在癌症基因图集(TCGA)和Liver-Kidney-Stomach(LKS)数据集上得到验证。拟议的方法在检索精度和速度两方面都优于Yottitxel(一个最新的其病理图象搜索引擎)。此外,我们在用于WSI分类的公共基准LKS数据集上取得了与STO的竞争性性表现。