Deep-learning-based object detection methods show promise for improving
screening mammography, but high rates of false positives can hinder their
effectiveness in clinical practice. To reduce false positives, we identify
three challenges: (1) unlike natural images, a malignant mammogram typically
contains only one malignant finding; (2) mammography exams contain two views of
each breast, and both views ought to be considered to make a correct
assessment; (3) most mammograms are negative and do not contain any findings.
In this work, we tackle the three aforementioned challenges by: (1) leveraging
Sparse R-CNN and showing that sparse detectors are more appropriate than dense
detectors for mammography; (2) including a multi-view cross-attention module to
synthesize information from different views; (3) incorporating multi-instance
learning (MIL) to train with unannotated images and perform breast-level
classification. The resulting model, M&M, is a Multi-view and Multi-instance
learning system that can both localize malignant findings and provide
breast-level predictions. We validate M&M's detection and classification
performance using five mammography datasets. In addition, we demonstrate the
effectiveness of each proposed component through comprehensive ablation
studies.

通过利用深度学习目标检测方法，我们提出了 M&M 这种多视角和多实例学习系统，以解决乳房 X 线摄影中错误阳性的问题，并在五个乳房 X 线摄影数据集上验证了其性能。

M&M: 用多视角和多实例学习稀疏检测器解决乳腺 X 光检查中的误报问题

M&M: Tackling False Positives in Mammography with a Multi-view and  Multi-instance Learning Sparse Detector

Along with the nearing completion of the Square Kilometre Array (SKA), comes
an increasing demand for accurate and reliable automated solutions to extract
valuable information from the vast amount of data it will allow acquiring.
Automated source finding is a particularly important task in this context, as
it enables the detection and classification of astronomical objects.
Deep-learning-based object detection and semantic segmentation models have
proven to be suitable for this purpose. However, training such deep networks
requires a high volume of labeled data, which is not trivial to obtain in the
context of radio astronomy. Since data needs to be manually labeled by experts,
this process is not scalable to large dataset sizes, limiting the possibilities
of leveraging deep networks to address several tasks. In this work, we propose
RADiff, a generative approach based on conditional diffusion models trained
over an annotated radio dataset to generate synthetic images, containing radio
sources of different morphologies, to augment existing datasets and reduce the
problems caused by class imbalances. We also show that it is possible to
generate fully-synthetic image-annotation pairs to automatically augment any
annotated dataset. We evaluate the effectiveness of this approach by training a
semantic segmentation model on a real dataset augmented in two ways: 1) using
synthetic images obtained from real masks, and 2) generating images from
synthetic semantic masks. We show an improvement in performance when applying
augmentation, gaining up to 18% in performance when using real masks and 4%
when augmenting with synthetic masks. Finally, we employ this model to generate
large-scale radio maps with the objective of simulating Data Challenges.

为了扩充现有的数据集并解决类别失衡的问题，我们提出了名为 RADiff 的条件扩散模型生成方法，用来生成包含不同形态的射电源的合成图像，同时还展示了我们训练深度学习模型并在数据集上实现语义分割任务的有效性。

可控扩散模型用于天文无线电图生成

RADiff: Controllable Diffusion Models for Radio Astronomical Maps  Generation

The consumption of microbial-contaminated food and water is responsible for
the deaths of millions of people annually. Smartphone-based microscopy systems
are portable, low-cost, and more accessible alternatives for the detection of
Giardia and Cryptosporidium than traditional brightfield microscopes. However,
the images from smartphone microscopes are noisier and require manual cyst
identification by trained technicians, usually unavailable in resource-limited
settings. Automatic detection of (oo)cysts using deep-learning-based object
detection could offer a solution for this limitation. We evaluate the
performance of three state-of-the-art object detectors to detect (oo)cysts of
Giardia and Cryptosporidium on a custom dataset that includes both smartphone
and brightfield microscopic images from vegetable samples. Faster RCNN,
RetinaNet, and you only look once (YOLOv8s) deep-learning models were employed
to explore their efficacy and limitations. Our results show that while the
deep-learning models perform better with the brightfield microscopy image
dataset than the smartphone microscopy image dataset, the smartphone microscopy
predictions are still comparable to the prediction performance of non-experts.

通过深度学习目标检测自动识别 Giardia 和 Cryptosporidium，可以替代传统显微镜检查，并在资源匮乏的地区提高检测效率。