for: This paper aims to investigate the differences in immune checkpoint expression between right and left colon cancer, and to identify potential biomarkers for immunotherapy.
methods: The study uses a dataset of 1756 images from 136 patients with colorectal cancer, stained with specific antibodies for CD3, CD8, CD45RO, PD-1, LAG3, and Tim3.
results: The paper finds that there are differences in immune checkpoint expression between right and left colon cancer, and identifies potential biomarkers for immunotherapy.Here is the information in Simplified Chinese text:
results: 论文发现右和左两侧colon癌免疫检查点表达有差异，并确定了可能的免疫治疗标记物。Abstract
Colorectal Cancer (CRC) is the second most common cause of cancer death in the world, ad can be identified by the location of the primary tumor in the large intestine: right and left colon, and rectum. Based on the location, CRC shows differences in chromosomal and molecular characteristics, microbiomes incidence, pathogenesis, and outcome. It has been shown that tumors on left and right sides also have different immune landscape, so the prognosis may be different based on the primary tumor locations. It is widely accepted that immune components of the tumor microenvironment (TME) plays a critical role in tumor development. One of the critical regulatory molecules in the TME is immune checkpoints that as the gatekeepers of immune responses regulate the infiltrated immune cell functions. Inhibitory immune checkpoints such as PD-1, Tim3, and LAG3, as the main mechanism of immune suppression in TME overexpressed and result in further development of the tumor. The images of this dataset have been taken from colon tissues of patients with CRC, stained with specific antibodies for CD3, CD8, CD45RO, PD-1, LAG3 and Tim3. The name of this dataset is CRC-ICM and contains 1756 images related to 136 patients. The initial version of CRC-ICM is published on Elsevier Mendeley dataset portal, and the latest version is accessible via: https://databiox.com
摘要
抗colon癌（CRC）是全球第二常见的癌症死亡原因，可以根据主 tumor 的位置在大肠中进行分类：右colon、左colon 和肛门。根据位置，CRC 会有不同的染色体和分子特征、微生物发生率、生物学过程和结果。有证据显示左和右方主 tumor 的免疫环境有所不同，因此预后可能因主 tumor 的位置而异。免疫组件在肿瘤微环境（TME）中扮演了重要的角色，并且免疫检查点（IC）是免疫回应的关键调节器。对 TME 中的免疫检查点进行抑制可以导致肿瘤的进一步发展。这些数据库包括1756幅图像，来自于136名患有CRC的病人的大肠标本，已经使用特定抗体进行染色，包括CD3、CD8、CD45RO、PD-1、LAG3 和 Tim3。这个数据库名为 CRC-ICM，可以在 Elsevier Mendeley 数据库 порталу上获取，或者通过以下连结：https://databiox.com。
Deformable-Detection Transformer for Microbubble Localization in Ultrasound Localization Microscopy
paper_authors: Sepideh K. Gharamaleki, Brandon Helfield, Hassan Rivaz
for: This paper aims to improve the localization of microbubbles (MBs) in ultrasound imaging, which is limited by the half-wavelength resolution of the imaging modality.
methods: The proposed method, DEformable DETR (DE-DETR), uses a multi-scale deformable attention mechanism to distribute attention within a limited budget, improving upon the conventional DETR approach which casts attention upon all grid pixels.
results: The proposed DE-DETR method shows improvement in both precision and recall, as well as the final super-resolution maps, compared to the conventional DETR method, when applied to the task of MB localization in ultrasound imaging.Abstract
To overcome the half a wavelength resolution limitations of ultrasound imaging, microbubbles (MBs) have been utilized widely in the field. Conventional MB localization methods are limited whether by exhaustive parameter tuning or considering a fixed Point Spread Function (PSF) for MBs. This questions their adaptability to different imaging settings or depths. As a result, development of methods that don't rely on manually adjusted parameters is crucial. Previously, we used a transformer-based approach i.e. DEtection TRansformer (DETR) (arXiv:2005.12872v3 and arXiv:2209.11859v1) to address the above mentioned issues. However, DETR suffers from long training times and lower precision for smaller objects. In this paper, we propose the application of DEformable DETR (DE-DETR) ( arXiv:2010.04159) for MB localization to mitigate DETR's above mentioned challenges. As opposed to DETR, where attention is casted upon all grid pixels, DE-DETR utilizes a multi-scale deformable attention to distribute attention within a limited budget. To evaluate the proposed strategy, pre-trained DE-DETR was fine-tuned on a subset of the dataset provided by the IEEE IUS Ultra-SR challenge organizers using transfer learning principles and subsequently we tested the network on the rest of the dataset, excluding the highly correlated frames. The results manifest an improvement both in precision and recall and the final super-resolution maps compared to DETR.
摘要
为了超越ultrasound imaging中半波长resolution的限制，广泛使用了微气泡（MBs）。传统的MBlocalization方法受限于手动调整的参数或者considering a fixed Point Spread Function（PSF）for MBs。这问题了其适应不同的imaging setting或深度。因此，开发不依赖于手动调整参数的方法是关键。在过去，我们使用了transformer-basedapproach，即DEtection TRansformer（DETR）（arXiv:2005.12872v3和arXiv:2209.11859v1）来解决上述问题。然而，DETR受到训练时间过长和对小对象的精度较低的问题。在这篇论文中，我们提议使用DEformable DETR（DE-DETR）（arXiv:2010.04159）来进行MB localization，以mitigate DETR的上述问题。与DETR不同，DE-DETR使用多尺度可变注意力来分配注意力，而不是对所有的网格像素进行注意力投入。为评估提议的策略，我们先在一个IEEE IUS Ultra-SR challenge提供的数据集上使用了转移学习原理进行先training DE-DETR，然后对剩下的数据集进行测试，排除高相关性的帧。结果显示，与DETR相比，DE-DETR在精度和准确性方面具有显著改进，并且最终的超高分辨率图像也得到了改进。
Cross-modality Attention-based Multimodal Fusion for Non-small Cell Lung Cancer (NSCLC) Patient Survival Prediction
results: 在非小细胞肺癌（NSCLC）患者存活预测 task 中，提出的融合策略比单 modal 学习提高了性能，c-index 从 0.5772 和 0.5885 提高到 0.6587。Abstract
Cancer prognosis and survival outcome predictions are crucial for therapeutic response estimation and for stratifying patients into various treatment groups. Medical domains concerned with cancer prognosis are abundant with multiple modalities, including pathological image data and non-image data such as genomic information. To date, multimodal learning has shown potential to enhance clinical prediction model performance by extracting and aggregating information from different modalities of the same subject. This approach could outperform single modality learning, thus improving computer-aided diagnosis and prognosis in numerous medical applications. In this work, we propose a cross-modality attention-based multimodal fusion pipeline designed to integrate modality-specific knowledge for patient survival prediction in non-small cell lung cancer (NSCLC). Instead of merely concatenating or summing up the features from different modalities, our method gauges the importance of each modality for feature fusion with cross-modality relationship when infusing the multimodal features. Compared with single modality, which achieved c-index of 0.5772 and 0.5885 using solely tissue image data or RNA-seq data, respectively, the proposed fusion approach achieved c-index 0.6587 in our experiment, showcasing the capability of assimilating modality-specific knowledge from varied modalities.
摘要
cancer 诊断和生存结果预测是致命的，它们对于治疗响应的估计和患者分配到不同的治疗组有重要作用。医疗领域对抗癌症的诊断和预测充满多种多样的数据，包括生物像数据和非生物像数据，如基因信息。到目前为止，多模态学习已经显示出了提高临床预测模型性能的潜力，通过提取和综合不同模态的信息。这种方法可能超越单模态学习，从而改善计算机辅助诊断和预测在各种医疗应用中的性能。在这项工作中，我们提出了一种跨模态注意力基于多模态融合管道，用于整合不同模态知识以提高患者存活预测的准确性。不同于将各模态特征直接拼接或加权平均，我们的方法评估每个模态对于特征融合的重要性，并在融合多模态特征时进行跨模态关系的权重评估。与单模态学习相比，我们的融合方法在实验中达到了c-指数0.6587，这说明了我们能够充分利用不同模态之间的关系，从而提高预测的准确性。