Panoramic image segmentation in computational pathology presents a remarkable
challenge due to the morphologically complex and variably scaled anatomy. For
instance, the intricate organization in kidney pathology spans multiple layers,
from regions like the cortex and medulla to functional units such as glomeruli,
tubules, and vessels, down to various cell types. In this paper, we propose a
novel Hierarchical Adaptive Taxonomy Segmentation (HATs) method, which is
designed to thoroughly segment panoramic views of kidney structures by
leveraging detailed anatomical insights. Our approach entails (1) the
innovative HATs technique which translates spatial relationships among 15
distinct object classes into a versatile "plug-and-play" loss function that
spans across regions, functional units, and cells, (2) the incorporation of
anatomical hierarchies and scale considerations into a unified simple matrix
representation for all panoramic entities, (3) the adoption of the latest AI
foundation model (EfficientSAM) as a feature extraction tool to boost the
model's adaptability, yet eliminating the need for manual prompt generation in
conventional segment anything model (SAM). Experimental findings demonstrate
that the HATs method offers an efficient and effective strategy for integrating
clinical insights and imaging precedents into a unified segmentation model
across more than 15 categories. The official implementation is publicly
available at this https URL

利用详细的解剖学见解，我们提出了一种新颖的分层自适应分类分割 (HATs) 方法，用于彻底地分割肾结构的全景视图。实验结果表明，HATs 方法为将临床见解和图像先例整合到超过 15 个类别的统一分割模型中提供了一种高效且有效的策略。

HATs：全景病理图像分析的分层自适应分类系统

HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic  Pathology Image Analysis

Convolutional Networks (ConvNets) excel at semantic segmentation and have
become a vital component for perception in autonomous driving. Enabling an
all-encompassing view of street-scenes, omnidirectional cameras present
themselves as a perfect fit in such systems. Most segmentation models for
parsing urban environments operate on common, narrow Field of View (FoV)
images. Transferring these models from the domain they were designed for to
360-degree perception, their performance drops dramatically, e.g., by an
absolute 30.0% (mIoU) on established test-beds. To bridge the gap in terms of
FoV and structural distribution between the imaging domains, we introduce
Efficient Concurrent Attention Networks (ECANets), directly capturing the
inherent long-range dependencies in omnidirectional imagery. In addition to the
learned attention-based contextual priors that can stretch across 360-degree
images, we upgrade model training by leveraging multi-source and
omni-supervised learning, taking advantage of both: Densely labeled and
unlabeled data originating from multiple datasets. To foster progress in
panoramic image segmentation, we put forward and extensively evaluate models on
Wild PAnoramic Semantic Segmentation (WildPASS), a dataset designed to capture
diverse scenes from all around the globe. Our novel model, training regimen and
multi-source prediction fusion elevate the performance (mIoU) to new
state-of-the-art results on the public PASS (60.2%) and the fresh WildPASS
(69.0%) benchmarks.

本文描述了一种名为 ECANets 的新型神经网络模型，它能够通过捕获全景图像中内在的长程相关性来对城市环境进行分割，以及该模型的训练策略和评估结果。