Precise localization of coronary arteries in Computed Tomography (CT) scans
is critical from the perspective of medical assessment of coronary artery
disease. Although various methods exist that offer high-quality segmentation of
coronary arteries in cardiac contrast-enhanced CT scans, the potential of less
invasive, non-contrast CT in this area is still not fully exploited. Since such
fine anatomical structures are hardly visible in this type of medical images,
the existing methods are characterized by high recall and low precision, and
are used mainly for filtering of atherosclerotic plaques in the context of
calcium scoring. In this paper, we address this research gap and introduce a
deep learning algorithm for segmenting coronary arteries in multi-vendor
ECG-gated non-contrast cardiac CT images which benefits from a novel framework
for semi-automatic generation of Ground Truth (GT) via image registration. We
hypothesize that the proposed GT generation process is much more efficient in
this case than manual segmentation, since it allows for a fast generation of
large volumes of diverse data, which leads to well-generalizing models. To
investigate and thoroughly evaluate the segmentation quality based on such an
approach, we propose a novel method for manual mesh-to-image registration,
which is used to create our test-GT. The experimental study shows that the
trained model has significantly higher accuracy than the GT used for training,
and leads to the Dice and clDice metrics close to the interrater variability.

本文提出了一种基于深度学习算法的多供应商心电图门控无对比剂心脏 CT 图像冠状动脉分割的方法，该方法通过图像配准实现了半自动生成 Ground Truth，研究结果表明训练模型具有比训练时使用的 Ground Truth 更高的准确性，并且 Dice 和 clDice 指标接近评定者之间的差异。

无造影钙化计分 CT 图像中的冠状动脉分割利用深度学习

Coronary artery segmentation in non-contrast calcium scoring CT images  using deep learning

Large Vision-Language Model (LVLM) has seen burgeoning development and
increasing attention recently. In this paper, we propose a novel framework,
camo-perceptive vision-language framework (CPVLF), to explore whether LVLM can
generalize to the challenging camouflaged object detection (COD) scenario in a
training-free manner. During the process of generalization, we find that due to
hallucination issues within LVLM, it can erroneously perceive objects in
camouflaged scenes, producing counterfactual concepts. Moreover, as LVLM is not
specifically trained for the precise localization of camouflaged objects, it
exhibits a degree of uncertainty in accurately pinpointing these objects.
Therefore, we propose chain of visual perception, which enhances LVLM's
perception of camouflaged scenes from both linguistic and visual perspectives,
reducing the hallucination issue and improving its capability in accurately
locating camouflaged objects. We validate the effectiveness of CPVLF on three
widely used COD datasets, and the experiments show the potential of LVLM in the
COD task.

我们提出了一种新颖的框架，camo-perceptive 视觉语言框架（CPVLF），以探索 LVLM 在具有挑战性的伪装目标检测（COD）情景中是否可以在无需训练的情况下实现泛化。通过观察 LVLM 的泛化过程，我们发现由于其内部的幻觉问题，它会错误地感知伪装场景中的物体，产生虚假的概念。此外，由于 LVLM 并未专门针对伪装物体的精确定位进行训练，它在准确定位这些对象方面表现出一定的不确定性。因此，我们提出了一种链式视觉感知方法，从语言和视觉角度增强 LVLM 对伪装场景的感知，减少幻觉问题，并提高其准确定位伪装物体的能力。我们在三个广泛使用的 COD 数据集上验证了 CPVLF 的有效性，实验结果显示了 LVLM 在 COD 任务中的潜力。

大规模视觉语言模型的泛化与虚构通过迷彩镜头

Generalization and Hallucination of Large Vision-Language Models through  a Camouflaged Lens

Deep neural networks with alternating convolutional, max-pooling and
decimation layers are widely used in state of the art architectures for
computer vision. Max-pooling purposefully discards precise spatial information
in order to create features that are more robust, and typically organized as
lower resolution spatial feature maps. On some tasks, such as whole-image
classification, max-pooling derived features are well suited; however, for
tasks requiring precise localization, such as pixel level prediction and
segmentation, max-pooling destroys exactly the information required to perform
well. Precise localization may be preserved by shallow convnets without pooling
but at the expense of robustness. Can we have our max-pooled multi-layered cake
and eat it too? Several papers have proposed summation and concatenation based
methods for combining upsampled coarse, abstract features with finer features
to produce robust pixel level predictions. Here we introduce another model ---
dubbed Recombinator Networks --- where coarse features inform finer features
early in their formation such that finer features can make use of several
layers of computation in deciding how to use coarse features. The model is
trained once, end-to-end and performs better than summation-based
architectures, reducing the error from the previous state of the art on two
facial keypoint datasets, AFW and AFLW, by 30\% and beating the current
state-of-the-art on 300W without using extra data. We improve performance even
further by adding a denoising prediction model based on a novel convnet
formulation.

本文介绍了一种名为 “Recombinator Networks” 的模型，该模型可以在不影响鲁棒性的前提下，通过信息重组的方式实现对精确定位的支持，相较于传统的基于求和和拼接的方法，其精度有显著提升，并将误差降低了 30％。同时作者还提出了基于卷积神经网络的去噪预测模型，进一步提升了该模型的性能。