Advancements in machine learning (ML) have significantly revolutionized
medical image analysis, prompting hospitals to rely on external ML services.
However, the exchange of sensitive patient data, such as chest X-rays, poses
inherent privacy risks when shared with third parties. Addressing this concern,
we propose MedBlindTuner, a privacy-preserving framework leveraging fully
homomorphic encryption (FHE) and a data-efficient image transformer (DEiT).
MedBlindTuner enables the training of ML models exclusively on FHE-encrypted
medical images. Our experimental evaluation demonstrates that MedBlindTuner
achieves comparable accuracy to models trained on non-encrypted images,
offering a secure solution for outsourcing ML computations while preserving
patient data privacy. To the best of our knowledge, this is the first work that
uses data-efficient image transformers and fully homomorphic encryption in this
domain.

机器学习在医学图像分析方面的进步极大地改变了医疗行业，但向第三方共享敏感患者数据（例如胸部 X 光片）存在固有的隐私风险。本文提出了 MedBlindTuner，一个基于全同态加密（FHE）和数据高效图像变换器（DEiT）的隐私保护框架。MedBlindTuner 使得机器学习模型仅在 FHE 加密的医学图像上进行训练。我们的实验评估表明，MedBlindTuner 的准确率与在非加密图像上训练的模型相当，为外包机器学习计算提供了安全的解决方案，同时保护患者数据的隐私。据我们所知，这是在此领域中首次使用数据高效图像变换器和全同态加密的工作。

MedBlindTuner：基于 Transformer 和全同态加密的隐私保护生物医学图像微调

MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical  Images with Transformers and Fully Homomorphic Encryption

Medical students and junior surgeons often rely on senior surgeons and
specialists to answer their questions when learning surgery. However, experts
are often busy with clinical and academic work, and have little time to give
guidance. Meanwhile, existing deep learning (DL)-based surgical Visual Question
Answering (VQA) systems can only provide simple answers without the location of
the answers. In addition, vision-language (ViL) embedding is still a less
explored research in these kinds of tasks. Therefore, a surgical Visual
Question Localized-Answering (VQLA) system would be helpful for medical
students and junior surgeons to learn and understand from recorded surgical
videos. We propose an end-to-end Transformer with Co-Attention gaTed
Vision-Language (CAT-ViL) for VQLA in surgical scenarios, which does not
require feature extraction through detection models. The CAT-ViL embedding
module is designed to fuse heterogeneous features from visual and textual
sources. The fused embedding will feed a standard Data-Efficient Image
Transformer (DeiT) module, before the parallel classifier and detector for
joint prediction. We conduct the experimental validation on public surgical
videos from MICCAI EndoVis Challenge 2017 and 2018. The experimental results
highlight the superior performance and robustness of our proposed model
compared to the state-of-the-art approaches. Ablation studies further prove the
outstanding performance of all the proposed components. The proposed method
provides a promising solution for surgical scene understanding, and opens up a
primary step in the Artificial Intelligence (AI)-based VQLA system for surgical
training. Our code is publicly available.

本研究提出了一种基于端到端 Transformer 与协同注意力门控视觉 - 语言嵌入的外科手术场景中可定位答案的视觉问答系统，该方法不需要通过检测模型进行特征提取，并建立在数据高效图像 Transformer 模块、并行分类器和检测器之上，该方法在公共手术视频实验上的结果表明了与现有技术的优越性。

视觉问答机器人手术中采用共同关注门控视觉语言嵌入的本地化回答

Co-Attention Gated Vision-Language Embedding for Visual Question  Localized-Answering in Robotic Surgery

Deep Metric Learning (DML) models rely on strong representations and
similarity-based measures with specific loss functions. Proxy-based losses have
shown great performance compared to pair-based losses in terms of convergence
speed. However, proxies that are assigned to different classes may end up being
closely located in the embedding space and hence having a hard time to
distinguish between positive and negative items. Alternatively, they may become
highly correlated and hence provide redundant information with the model. To
address these issues, we propose a novel approach that introduces Soft
Orthogonality (SO) constraint on proxies. The constraint ensures the proxies to
be as orthogonal as possible and hence control their positions in the embedding
space. Our approach leverages Data-Efficient Image Transformer (DeiT) as an
encoder to extract contextual features from images along with a DML objective.
The objective is made of the Proxy Anchor loss along with the SO
regularization. We evaluate our method on four public benchmarks for
category-level image retrieval and demonstrate its effectiveness with
comprehensive experimental results and ablation studies. Our evaluations
demonstrate the superiority of our proposed approach over state-of-the-art
methods by a significant margin.

本文提出了一种基于 Soft Orthogonality (简称 SO) 的新方法来解决深度度量学习 (DML) 中代理点出现的问题，并结合数据高效的图像变换器 (DeiT) 作为编码器提取图片的上下文特征，以数据集检索等实验结果表明该方法在超越现有技术的同时，还具有更高的可靠性和实用性