Digital mammography is essential to breast cancer detection, and deep
learning offers promising tools for faster and more accurate mammogram
analysis. In radiology and other high-stakes environments, uninterpretable
("black box") deep learning models are unsuitable and there is a call in these
fields to make interpretable models. Recent work in interpretable computer
vision provides transparency to these formerly black boxes by utilizing
prototypes for case-based explanations, achieving high accuracy in applications
including mammography. However, these models struggle with precise feature
localization, reasoning on large portions of an image when only a small part is
relevant. This paper addresses this gap by proposing a novel multi-scale
interpretable deep learning model for mammographic mass margin classification.
Our contribution not only offers an interpretable model with reasoning aligned
with radiologist practices, but also provides a general architecture for
computer vision with user-configurable prototypes from coarse- to fine-grained
prototypes.

数字乳房 X 线摄影是乳腺癌检测的关键，而深度学习为更快、更准确的乳房 X 线分析提供了有前途的工具。然而，在放射学和其他高风险环境中，不可解释的（“黑盒子”）深度学习模型不适用，并且这些领域对于希望创建可解释模型的呼声很高。最近在可解释计算机视觉方面的研究通过利用案例解释中的原型实现透明性，提供了高准确性的应用，包括乳房 X 线摄影。然而，这些模型在精确特征定位方面存在困难，当只有一小部分图像相关时，需要对图像的大部分进行推理。本文通过提出一种新的多尺度可解释深度学习模型来解决这一差距，用于乳房 X 线摄影的肿块边缘分类。我们的贡献不仅提供了一个可解释的模型，其推理与放射科医生的实践相吻合，而且提供了一个通用架构，用于从粗粒度到细粒度的用户可配置的原型。

FPN-IAIA-BL: 数字乳房 X 线照片乳房肿块边界分类的多尺度可解释性深度学习模型

FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for  Classification of Mass Margins in Digital Mammography

Early works on medical image compression date to the 1980's with the impetus
on deployment of teleradiology systems for high-resolution digital X-ray
detectors. Commercially deployed systems during the period could compress 4,096
x 4,096 sized images at 12 bpp to 2 bpp using lossless arithmetic coding, and
over the years JPEG and JPEG2000 were imbibed reaching upto 0.1 bpp. Inspired
by the reprise of deep learning based compression for natural images over the
last two years, we propose a fully convolutional autoencoder for diagnostically
relevant feature preserving lossy compression. This is followed by leveraging
arithmetic coding for encapsulating high redundancy of features for further
high-density code packing leading to variable bit length. We demonstrate
performance on two different publicly available digital mammography datasets
using peak signal-to-noise ratio (pSNR), structural similarity (SSIM) index and
domain adaptability tests between datasets. At high density compression factors
of >300x (~0.04 bpp), our approach rivals JPEG and JPEG2000 as evaluated
through a Radiologist's visual Turing test.

使用全卷积自编码器结合算术编码实现医学影像的有损压缩，并在公开数据集上进行性能测试，最高压缩比可达 300 倍。