In recent years, various large foundation models have been proposed for image
segmentation. There models are often trained on large amounts of data
corresponding to general computer vision tasks. Hence, these models do not
perform well on medical data. There have been some attempts in the literature
to perform parameter-efficient finetuning of such foundation models for medical
image segmentation. However, these approaches assume that all the parameters of
the model are available for adaptation. But, in many cases, these models are
released as APIs or blackboxes, with no or limited access to the model
parameters and data. In addition, finetuning methods also require a significant
amount of compute, which may not be available for the downstream task. At the
same time, medical data can't be shared with third-party agents for finetuning
due to privacy reasons. To tackle these challenges, we pioneer a blackbox
adaptation technique for prompted medical image segmentation, called BAPS. BAPS
has two components - (i) An Image-Prompt decoder (IP decoder) module that
generates visual prompts given an image and a prompt, and (ii) A Zero Order
Optimization (ZOO) Method, called SPSA-GC that is used to update the IP decoder
without the need for backpropagating through the foundation model. Thus, our
method does not require any knowledge about the foundation model's weights or
gradients. We test BAPS on four different modalities and show that our method
can improve the original model's performance by around 4%.

在医学图像分割中，通过使用黑盒适应技术 (BAPS)，我们不需要了解基础模型的权重或梯度，并且可以显着提高原始模型的性能约 4%。

医学图像分割的黑盒自适应

Blackbox Adaptation for Medical Image Segmentation

We consider the problem of structure learning for bow-free acyclic path
diagrams (BAPs). BAPs can be viewed as a generalization of linear Gaussian DAG
models that allow for certain hidden variables. We present a first method for
this problem using a greedy score-based search algorithm. We also prove some
necessary and some sufficient conditions for distributional equivalence of BAPs
which are used in an algorithmic ap- proach to compute (nearly) equivalent
model structures. This allows us to infer lower bounds of causal effects. We
also present applications to real and simulated datasets using our publicly
available R-package.

本文研究了基于得分的贪心搜索算法用于学习无环路径图的结构，并且证明了一些 BAPs 分布等价（distributional equivalence）的必要和充分条件，这种算法可以推断因果效应的下界，同时通过真实数据和模拟数据的应用进行了验证。