In the realm of medical 3D data, such as CT and MRI images, prevalent
anisotropic resolution is characterized by high intra-slice but diminished
inter-slice resolution. The lowered resolution between adjacent slices poses
challenges, hindering optimal viewing experiences and impeding the development
of robust downstream analysis algorithms. Various volumetric super-resolution
algorithms aim to surmount these challenges, enhancing inter-slice resolution
and overall 3D medical imaging quality. However, existing approaches confront
inherent challenges: 1) often tailored to specific upsampling factors, lacking
flexibility for diverse clinical scenarios; 2) newly generated slices
frequently suffer from over-smoothing, degrading fine details, and leading to
inter-slice inconsistency. In response, this study presents CycleINR, a novel
enhanced Implicit Neural Representation model for 3D medical data volumetric
super-resolution. Leveraging the continuity of the learned implicit function,
the CycleINR model can achieve results with arbitrary up-sampling rates,
eliminating the need for separate training. Additionally, we enhance the grid
sampling in CycleINR with a local attention mechanism and mitigate
over-smoothing by integrating cycle-consistent loss. We introduce a new metric,
Slice-wise Noise Level Inconsistency (SNLI), to quantitatively assess
inter-slice noise level inconsistency. The effectiveness of our approach is
demonstrated through image quality evaluations on an in-house dataset and a
downstream task analysis on the Medical Segmentation Decathlon liver tumor
dataset.

本研究提出了 CycleINR，这是一种新颖的增强隐式神经表示模型，用于 3D 医学数据体积超分辨率。通过利用学习的隐式函数的连续性，CycleINR 模型可以以任意上采样率获得结果，消除了单独训练的需求。此外，我们通过引入局部注意机制和融合循环一致性损失来增强 CycleINR 中的格网采样，并减轻过度平滑。我们引入了一个新的度量标准，即切片噪声水平不一致性（SNLI），来定量评估切片间噪声水平的不一致性。我们通过对内部数据集的图像质量评估和对医学分割十项全能（Decathlon）肝肿瘤数据集的下游任务分析来证明我们方法的有效性。

CycleINR: 周期隐式神经表示实现医学数据任意尺度体积超分辨率

CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale  Volumetric Super-Resolution of Medical Data

Often, applications of self-supervised learning to 3D medical data opt to use
3D variants of successful 2D network architectures. Although promising
approaches, they are significantly more computationally demanding to train, and
thus reduce the widespread applicability of these methods away from those with
modest computational resources. Thus, in this paper, we aim to improve standard
2D SSL algorithms by modelling the inherent 3D nature of these datasets
implicitly. We propose two variants that build upon a strong baseline model and
show that both of these variants often outperform the baseline in a variety of
downstream tasks. Importantly, in contrast to previous works in both 2D and 3D
approaches for 3D medical data, both of our proposals introduce negligible
additional overhead over the baseline, improving the democratisation of these
approaches for medical applications.

本文旨在改进标准的 2D 自监督学习算法，通过对数据集的内在 3D 特性进行建模来解决在 3D 医疗数据中应用的计算资源问题，并且提出了两个改进版本，证明在各种下游任务中，这两个版本往往优于基准模型，同时也几乎不带来额外的开销，提高了医疗应用中这些方法的普及性。

DeepSet SimCLR：用于改进病理学表示学习的自监督深度集合

DeepSet SimCLR: Self-supervised deep sets for improved pathology  representation learning

The automatic classification of 3D medical data is memory-intensive. Also,
variations in the number of slices between samples is common. Naive solutions
such as subsampling can solve these problems, but at the cost of potentially
eliminating relevant diagnosis information. Transformers have shown promising
performance for sequential data analysis. However, their application for
long-sequences is data, computationally, and memory demanding. In this paper,
we propose an end-to-end Transformer-based framework that allows to classify
volumetric data of variable length in an efficient fashion. Particularly, by
randomizing the input slice-wise resolution during training, we enhance the
capacity of the learnable positional embedding assigned to each volume slice.
Consequently, the accumulated positional information in each positional
embedding can be generalized to the neighbouring slices, even for high
resolution volumes at the test time. By doing so, the model will be more robust
to variable volume length and amenable to different computational budgets. We
evaluated the proposed approach in retinal OCT volume classification and
achieved 21.96% average improvement in balanced accuracy on a 9-class
diagnostic task, compared to state-of-the-art video transformers. Our findings
show that varying the slice-wise resolution of the input during training
results in more informative volume representation as compared to training with
fixed number of slices per volume. Our code is available at:
this https URL

本研究提出一种基于 Transformer 的框架，通过随机分辨率输入不同的切片来增强可学习的位置嵌入，从而可以有效地分类可变长度的体积数据，这种方法在视网膜 OCT 卷积分类上获得了 21.96% 的平衡准确度提升。