This study presents an unsupervised domain adaptation method aimed at
autonomously generating image masks outlining regions of interest (ROIs) for
differentiating breast lesions in breast ultrasound (US) imaging. Our
semi-supervised learning approach utilizes a primitive model trained on a small
public breast US dataset with true annotations. This model is then iteratively
refined for the domain adaptation task, generating pseudo-masks for our
private, unannotated breast US dataset. The dataset, twice the size of the
public one, exhibits considerable variability in image acquisition perspectives
and demographic representation, posing a domain-shift challenge. Unlike typical
domain adversarial training, we employ downstream classification outcomes as a
benchmark to guide the updating of pseudo-masks in subsequent iterations. We
found the classification precision to be highly correlated with the
completeness of the generated ROIs, which promotes the explainability of the
deep learning classification model. Preliminary findings demonstrate the
efficacy and reliability of this approach in streamlining the ROI annotation
process, thereby enhancing the classification and localization of breast
lesions for more precise and interpretable diagnoses.

本研究提出了一种无监督领域适应方法，用于自动生成乳腺超声成像中乳腺病变感兴趣区域（ROIs）的图像掩膜。我们的半监督学习方法利用在带有真实注释的小型公共乳腺超声数据集上训练的原始模型，针对领域适应任务进行迭代改进，生成私人未标记乳腺超声数据集的伪掩膜。该数据集的大小是公共数据集的两倍，并在图像采集角度和人群代表性方面具有相当的变异性，具有领域转移的挑战。与典型的领域对抗训练不同的是，我们使用下游分类结果作为基准，指导随后迭代中伪掩膜的更新。我们发现分类精度与生成的 ROIs 的完整性之间高度相关，促进了深度学习分类模型的解释性。初步研究结果证明了这种方法在简化 ROI 注释过程，从而增强乳腺病变的分类和定位以进行更精确和可解释的诊断方面的效果和可靠性。

增强人工智能诊断：半监督深度学习自主病变掩模

Enhancing AI Diagnostics: Autonomous Lesion Masking via Semi-Supervised  Deep Learning

We demonstrate MaskSearch, a system designed to accelerate queries over
databases of image masks generated by machine learning models. MaskSearch
formalizes and accelerates a new category of queries for retrieving images and
their corresponding masks based on mask properties, which support various
applications, from identifying spurious correlations learned by models to
exploring discrepancies between model saliency and human attention. This
demonstration makes the following contributions:(1) the introduction of
MaskSearch's graphical user interface (GUI), which enables interactive
exploration of image databases through mask properties, (2) hands-on
opportunities for users to explore MaskSearch's capabilities and constraints
within machine learning workflows, and (3) an opportunity for conference
attendees to understand how MaskSearch accelerates queries over image masks.

我们展示了 MaskSearch，这是一个用于加速机器学习模型生成的图像掩码数据库的查询的系统。MaskSearch 通过掩码属性形式化和加速了一类新的查询，用于根据掩码属性检索图像及其对应的掩码，支持从识别模型学习到的虚假相关性到探索模型显著性和人类注意力之间的差异等各种应用。

展示 MaskSearch：高效查询图像蒙版的机器学习工作流程

Demonstration of MaskSearch: Efficiently Querying Image Masks for  Machine Learning Workflows

TomoSAM has been developed to integrate the cutting-edge Segment Anything
Model (SAM) into 3D Slicer, a highly capable software platform used for 3D
image processing and visualization. SAM is a promptable deep learning model
that is able to identify objects and create image masks in a zero-shot manner,
based only on a few user clicks. The synergy between these tools aids in the
segmentation of complex 3D datasets from tomography or other imaging
techniques, which would otherwise require a laborious manual segmentation
process. The source code associated with this article can be found at
this https URL

TomoSAM 是一个将前沿的 Segment Anything Model 集成到 3D Slicer 中的软件，它是一个能够在零样本学习的基础上，基于用户点击来识别物体和创建图像 mask 的深度学习模型，这个工具的协同作用有助于对来自断层扫描或其他成像技术的复杂 3D 数据集进行分割。