Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning (1506.05032v5)

Abstract: In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. {Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available

• The paper introduces a DFDL framework that learns class-specific dictionaries with sparsity constraints to enhance image classification accuracy.
• It demonstrates a low-complexity approach validated on real-world databases including breast lesions, organ images, and brain tumors.
• The method outperforms state-of-the-art techniques by maintaining stable accuracy even with fewer training samples, aiding medical diagnostics.

The paper "Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning" addresses a significant challenge in the field of histopathological image analysis: the extraction of robust features suitable for classification tasks. The diversity in histological features and the rich geometrical structures present in these images make this extraction particularly complex.

To tackle this, the authors propose an innovative framework known as Discriminative Feature-oriented Dictionary Learning (DFDL). This method focuses on learning class-specific dictionaries which enable the efficient and parsimonious representation of new image samples. The key idea is that these dictionaries, when bound by a sparsity constraint, will represent new images with high accuracy only if the images belong to the same class as the dictionary. Conversely, the dictionaries will perform poorly for representing images from other classes.

The paper outlines a low-complexity classification method based on these learned dictionaries and applies it to the grading of diseases depicted in histopathological images. The merits of DFDL are demonstrated through experiments on three real-world image databases:

1. Histopathological images of intraductal breast lesions.
2. Images of mammalian kidney, lung, and spleen provided by the Animal Diagnostics Lab at Pennsylvania State University.
3. Brain tumor images from The Cancer Genome Atlas (TCGA) database.

The experimental results indicate that DFDL not only outperforms state-of-the-art alternatives but also maintains a more stable classification accuracy as the number of training images decreases. This resilience is particularly beneficial in practical scenarios where ample training data may not be available.

Overall, the DFDL method presented in this paper shows significant promise for advancing automatic feature extraction and classification in histopathological image analysis, offering a practical tool for medical diagnostics and research.