Pathology image are essential for accurately interpreting lesion cells in
cytopathology screening, but acquiring high-resolution digital slides requires
specialized equipment and long scanning times. Though super-resolution (SR)
techniques can alleviate this problem, existing deep learning models recover
pathology image in a black-box manner, which can lead to untruthful biological
details and misdiagnosis. Additionally, current methods allocate the same
computational resources to recover each pixel of pathology image, leading to
the sub-optimal recovery issue due to the large variation of pathology image.
In this paper, we propose the first hierarchical reinforcement learning
framework named Spatial-Temporal hierARchical Reinforcement Learning (STAR-RL),
mainly for addressing the aforementioned issues in pathology image
super-resolution problem. We reformulate the SR problem as a Markov decision
process of interpretable operations and adopt the hierarchical recovery
mechanism in patch level, to avoid sub-optimal recovery. Specifically, the
higher-level spatial manager is proposed to pick out the most corrupted patch
for the lower-level patch worker. Moreover, the higher-level temporal manager
is advanced to evaluate the selected patch and determine whether the
optimization should be stopped earlier, thereby avoiding the over-processed
problem. Under the guidance of spatial-temporal managers, the lower-level patch
worker processes the selected patch with pixel-wise interpretable actions at
each time step. Experimental results on medical images degraded by different
kernels show the effectiveness of STAR-RL. Furthermore, STAR-RL validates the
promotion in tumor diagnosis with a large margin and shows generalizability
under various degradations. The source code is available at
this https URL

提出了一种层次强化学习框架，用于解决病理图像超分辨率问题中的问题，通过分级恢复机制和时空管理器实现对病理图像的逐块恢复，并在肿瘤诊断方面取得显著提升。

时空分层强化学习用于可解释的病理图像超分辨率

Spatial-temporal Hierarchical Reinforcement Learning for Interpretable  Pathology Image Super-Resolution

Glioblastoma is the most common primary adult brain tumor, with a grim
prognosis - median survival of 12-18 months following treatment, and 4 months
otherwise. Glioblastoma is widely infiltrative in the cerebral hemispheres and
well-defined by heterogeneous molecular and micro-environmental histopathologic
profiles, which pose a major obstacle in treatment. Correctly diagnosing these
tumors and assessing their heterogeneity is crucial for choosing the precise
treatment and potentially enhancing patient survival rates. In the
gold-standard histopathology-based approach to tumor diagnosis, detecting
various morpho-pathological features of distinct histology throughout digitized
tissue sections is crucial. Such "features" include the presence of cellular
tumor, geographic necrosis, pseudopalisading necrosis, areas abundant in
microvascular proliferation, infiltration into the cortex, wide extension in
subcortical white matter, leptomeningeal infiltration, regions dense with
macrophages, and the presence of perivascular or scattered lymphocytes. With
these features in mind and building upon the main aim of the BraTS Cluster of
Challenges this https URL, the goal of the BraTS-Path
challenge is to provide a systematically prepared comprehensive dataset and a
benchmarking environment to develop and fairly compare deep-learning models
capable of identifying tumor sub-regions of distinct histologic profile. These
models aim to further our understanding of the disease and assist in the
diagnosis and grading of conditions in a consistent manner.

BrATs-Path 挑战的主要目标是为开发和公平比较能够识别具有不同组织学特征的肿瘤亚区的深度学习模型提供系统准备的完整数据集和基准环境，以进一步了解该疾病并以一致的方式协助诊断和分级。

BraTS-Path 挑战赛：评估非均质组织病理性脑肿瘤亚区

BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain  Tumor Sub-regions

Nakagami imaging holds promise for visualizing and quantifying tissue
scattering in ultrasound waves, with potential applications in tumor diagnosis
and fat fraction estimation which are challenging to discern by conventional
ultrasound B-mode images. Existing methods struggle with optimal window size
selection and suffer from estimator instability, leading to degraded resolution
images. To address this, here we propose a novel method called UNICORN
(Ultrasound Nakagami Imaging via Score Matching and Adaptation), that offers an
accurate, closed-form estimator for Nakagami parameter estimation in terms of
the score function of ultrasonic envelope. Extensive experiments using
simulation and real ultrasound RF data demonstrate UNICORN's superiority over
conventional approaches in accuracy and resolution quality.

提出了一种基于 UNICORN 方法的新颖方法，通过分数匹配和适应性来准确估计 Nakagami 参数，以实现超声波成像的高准确性和分辨率品质。