Causal disentanglement has great potential for capturing complex situations.
However, there is a lack of practical and efficient approaches. It is already
known that most unsupervised disentangling methods are unable to produce
identifiable results without additional information, often leading to randomly
disentangled output. Therefore, most existing models for disentangling are
weakly supervised, providing information about intrinsic factors, which incurs
excessive costs. Therefore, we propose a novel model, SCADI(SElf-supervised
CAusal DIsentanglement), that enables the model to discover semantic factors
and learn their causal relationships without any supervision. This model
combines a masked structural causal model (SCM) with a pseudo-label generator
for causal disentanglement, aiming to provide a new direction for
self-supervised causal disentanglement models.

提出了一种新的自监督因果解缠模型 SCADI，通过结合屏蔽的结构因果模型（SCM）和伪标签生成器，实现模型在没有任何监督的情况下发现语义因素并学习它们之间的因果关系。

SCADI: 自主监督下的因果分解潜变量模型

SCADI: Self-supervised Causal Disentanglement in Latent Variable Models

Inferring biological relationships from cellular phenotypes in high-content
microscopy screens provides significant opportunity and challenge in biological
research. Prior results have shown that deep vision models can capture
biological signal better than hand-crafted features. This work explores how
weakly supervised and self-supervised deep learning approaches scale when
training larger models on larger datasets. Our results show that both CNN- and
ViT-based masked autoencoders significantly outperform weakly supervised
models. At the high-end of our scale, a ViT-L/8 trained on over 3.5-billion
unique crops sampled from 95-million microscopy images achieves relative
improvements as high as 28% over our best weakly supervised models at inferring
known biological relationships curated from public databases.

通过高内容显微镜筛选从细胞表型中推断生物关系在生物研究中提供了重要的机遇和挑战。该研究探讨了在更大数据集上训练更大模型时，弱监督和自监督深度学习方法的可扩展性。结果显示，基于 CNN 和 ViT 的掩蔽自编码器在表型推断方面明显优于弱监督模型。在我们的尺度上限，通过来自 9500 万显微镜图像的 35 亿个独特裁剪样本训练的 ViT-L/8 相对于从公共数据库中整理的已知生物关系的最佳弱监督模型的相对改善率达到了 28%。

掩蔽自编码器是可扩展的细胞形态学学习器

Masked autoencoders are scalable learners of cellular morphology

In recent years, weakly supervised models have aided in mass detection using
mammography images, decreasing the need for pixel-level annotations. However,
most existing models in the literature rely on Class Activation Maps (CAM) as
the activation method, overlooking the potential benefits of exploring other
activation techniques. This work presents a study that explores and compares
different activation maps in conjunction with state-of-the-art methods for
weakly supervised training in mammography images. Specifically, we investigate
CAM, GradCAM, GradCAM++, XGradCAM, and LayerCAM methods within the framework of
the GMIC model for mass detection in mammography images. The evaluation is
conducted on the VinDr-Mammo dataset, utilizing the metrics Accuracy, True
Positive Rate (TPR), False Negative Rate (FNR), and False Positive Per Image
(FPPI). Results show that using different strategies of activation maps during
training and test stages leads to an improvement of the model. With this
strategy, we improve the results of the GMIC method, decreasing the FPPI value
and increasing TPR.

近年来，弱监督模型在使用乳腺 X 光照片进行肿瘤检测方面发挥了作用，减少了对像素级注释的需求。然而，大部分文献中的现有模型依赖于类别激活图 (CAM) 作为激活方法，忽视了探索其他激活技术的潜在益处。本研究探索并比较了不同的激活图与弱监督训练方法在乳腺 X 光照片中的检测效果。具体而言，我们在 GMIC 模型中研究了 CAM、GradCAM、GradCAM++、XGradCAM 和 LayerCAM 方法，并在 VinDr-Mammo 数据集上进行了评估，使用了精度、真阳性率 (TPR)、假阴性率 (FNR) 和每张图像的假阳性数 (FPPI) 这些度量指标。结果表明，在训练和测试阶段使用不同的激活图策略会改善模型性能。通过这种策略，我们改善了 GMIC 方法的结果，降低了 FPPI 值并增加了 TPR。