IEEE Trans Med Imaging. 2014 May;33(5):1163-79. doi: 10.1109/TMI.2014.2306173.
The multi-channel nature of digital histopathological images presents an opportunity to exploit the correlated color channel information for better image modeling. Inspired by recent work in sparsity for single channel image classification, we propose a new simultaneous sparsity model for multi-channel histopathological image representation and classification (SHIRC). Essentially, we represent a histopathological image as a sparse linear combination of training examples under suitable channel-wise constraints. Classification is performed by solving a newly formulated simultaneous sparsity-based optimization problem. A practical challenge is the correspondence of image objects (cellular and nuclear structures) at different spatial locations in the image. We propose a robust locally adaptive variant of SHIRC (LA-SHIRC) to tackle this issue. Experiments on two challenging real-world image data sets: 1) mammalian tissue images acquired by pathologists of the animal diagnostics lab (ADL) at Pennsylvania State University, and 2) human intraductal breast lesions, reveal the merits of our proposal over state-of-the-art alternatives. Further, we demonstrate that LA-SHIRC exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training per class is often not available.
数字组织病理学图像的多通道特性为利用相关颜色通道信息进行更好的图像建模提供了机会。受单通道图像分类中稀疏方法的启发，我们提出了一种新的用于多通道组织病理学图像表示和分类（SHIRC）的同时稀疏模型。本质上，我们将组织病理学图像表示为在适当的通道约束下对训练示例的稀疏线性组合。通过解决新制定的基于同时稀疏的优化问题来执行分类。一个实际的挑战是图像对象（细胞和核结构）在图像中不同空间位置的对应关系。我们提出了一种鲁棒的局部自适应 SHIRC（LA-SHIRC）变体来解决这个问题。在两个具有挑战性的真实世界图像数据集上进行的实验：1）宾夕法尼亚州立大学动物诊断实验室（ADL）的病理学家采集的哺乳动物组织图像，以及 2）人类乳腺导管内病变，证明了我们的提议优于最先进的替代方案。此外，我们证明 LA-SHIRC 显示出在训练图像数量上的分类准确性的更平滑衰减，这在实践中是非常理想的，因为通常无法为每个类别提供大量的训练。
