Multi-modal brain images from MRI scans are widely used in clinical diagnosis
to provide complementary information from different modalities. However,
obtaining fully paired multi-modal images in practice is challenging due to
various factors, such as time, cost, and artifacts, resulting in
modality-missing brain images. To address this problem, unsupervised
multi-modal brain image translation has been extensively studied. Existing
methods suffer from the problem of brain tumor deformation during translation,
as they fail to focus on the tumor areas when translating the whole images. In
this paper, we propose an unsupervised tumor-aware distillation teacher-student
network called UTAD-Net, which is capable of perceiving and translating tumor
areas precisely. Specifically, our model consists of two parts: a teacher
network and a student network. The teacher network learns an end-to-end mapping
from source to target modality using unpaired images and corresponding tumor
masks first. Then, the translation knowledge is distilled into the student
network, enabling it to generate more realistic tumor areas and whole images
without masks. Experiments show that our model achieves competitive performance
on both quantitative and qualitative evaluations of image quality compared with
state-of-the-art methods. Furthermore, we demonstrate the effectiveness of the
generated images on downstream segmentation tasks. Our code is available at
this https URL

本文提出了一种无监督的肿瘤感知蒸馏师生网络 UTAD-Net，通过教师网络学习源模态到目标模态的映射，然后将映射知识蒸馏到学生网络，使其能够生成更真实的肿瘤区域和完整图像，实验表明我们的模型在图像质量的定量和定性评估以及后续分割任务上表现出竞争性的性能。

无监督的肿瘤感知蒸馏用于多模态脑图像转换

Unsupervised Tumor-Aware Distillation for Multi-Modal Brain Image  Translation

The success of large language models has inspired the computer vision
community to explore image segmentation foundation model that is able to
zero/few-shot generalize through prompt engineering. Segment-Anything(SAM),
among others, is the state-of-the-art image segmentation foundation model
demonstrating strong zero/few-shot generalization. Despite the success, recent
studies reveal the weakness of SAM under strong distribution shift. In
particular, SAM performs awkwardly on corrupted natural images, camouflaged
images, medical images, etc. Motivated by the observations, we aim to develop a
self-training based strategy to adapt SAM to target distribution. Given the
unique challenges of large source dataset, high computation cost and incorrect
pseudo label, we propose a weakly supervised self-training architecture with
anchor regularization and low-rank finetuning to improve the robustness and
computation efficiency of adaptation. We validate the effectiveness on 5 types
of downstream segmentation tasks including natural clean/corrupted images,
medical images, camouflaged images and robotic images. Our proposed method is
task-agnostic in nature and outperforms pre-trained SAM and state-of-the-art
domain adaptation methods on almost all downstream tasks with the same testing
prompt inputs.

基于自训练的策略，通过锚点规范化和低秩微调，提升了图像分割基础模型的适应性和计算效率，并在多个下游分割任务中表现出优于预训练模型 SAM 和最先进的领域自适应方法的性能。

通过弱监督适应提高分割基础模型在分布变化下的泛化能力

Improving the Generalization of Segmentation Foundation Model under  Distribution Shift via Weakly Supervised Adaptation

Most previous approaches for analyzing food images have relied on extensively
annotated datasets, resulting in significant human labeling expenses due to the
varied and intricate nature of such images. Inspired by the effectiveness of
contrastive self-supervised methods in utilizing unlabelled data, weiqing
explore leveraging these techniques on unlabelled food images. In contrastive
self-supervised methods, two views are randomly generated from an image by data
augmentations. However, regarding food images, the two views tend to contain
similar informative contents, causing large mutual information, which impedes
the efficacy of contrastive self-supervised learning. To address this problem,
we propose Feature Suppressed Contrast (FeaSC) to reduce mutual information
between views. As the similar contents of the two views are salient or highly
responsive in the feature map, the proposed FeaSC uses a response-aware scheme
to localize salient features in an unsupervised manner. By suppressing some
salient features in one view while leaving another contrast view unchanged, the
mutual information between the two views is reduced, thereby enhancing the
effectiveness of contrast learning for self-supervised food pre-training. As a
plug-and-play module, the proposed method consistently improves BYOL and
SimSiam by 1.70\% $\sim$ 6.69\% classification accuracy on four publicly
available food recognition datasets. Superior results have also been achieved
on downstream segmentation tasks, demonstrating the effectiveness of the
proposed method.

通过对食物图像进行对比自监督学习，提出了一种减少视图间相互信息的方法（FeaSC），并且在公开食物识别数据集上提高了分类准确度，并在下游分割任务上取得了优秀的结果。