RECENT CNN-BASED TECHNIQUES FOR BREAST CANCER HISTOLOGY IMAGE CLASSIFICATION

Histology images are extensively used by pathologists to assess abnormalities and detect malignancy in breast tissues. On the other hand, Convolutional Neural Networks (CNN) are by far, the privileged models for image classification and interpretation. Based on these two facts, we surveyed the recent CNN-based methods for breast cancer histology image analysis. The survey focuses on two major issues usually faced by CNN-based methods namely the design of an appropriate CNN architecture and the lack of a sufficient labelled dataset for training the model. Regarding the design of the CNN architecture, methods examining breast histology images adopt three main approaches: Designing manually from scratch the CNN architecture, using pre-trained models and adopting an automatic architecture design. Methods addressing the lack of labelled datasets are grouped into four categories: methods using pre-trained models, methods using data augmentation, methods adopting weakly supervised learning and those adopting feedforward filter learning. Research works from each category and reported performance are presented in this paper. We conclude the paper by indicating some future research directions related to the analysis of histology images

Metric Learning in Histopathological Image Classification: Opening the Black Box

The application of machine learning techniques to histopathology images enables advances in the field, providing valuable tools that can speed up and facilitate the diagnosis process. The classification of these images is a relevant aid for physicians who have to process a large number of images in long and repetitive tasks. This work proposes the adoption of metric learning that, beyond the task of classifying images, can provide additional information able to support the decision of the classification system. In particular, triplet networks have been employed to create a representation in the embedding space that gathers together images of the same class while tending to separate images with different labels. The obtained representation shows an evident separation of the classes with the possibility of evaluating the similarity and the dissimilarity among input images according to distance criteria. The model has been tested on the BreakHis dataset, a reference and largely used dataset that collects breast cancer images with eight pathology labels and four magnification levels. Our proposed classification model achieves relevant performance on the patient level, with the advantage of providing interpretable information for the obtained results, which represent a specific feature missed by the all the recent methodologies proposed for the same purpose

Fisher encoding of convolutional neural network features for endoscopic image classification

We propose an approach for the automated diagnosis of celiac disease (CD) and colonic polyps (CP) based on applying Fisher encoding to the activations of convolutional layers. In our experiments, three different convolutional neural network (CNN) architectures (AlexNet, VGG-f, and VGG-16) are applied to three endoscopic image databases (one CD database and two CP databases). For each network architecture, we perform experiments using a version of the net that is pretrained on the ImageNet database, as well as a version of the net that is trained on a specific endoscopic image database. The Fisher representations of convolutional layer activations are classified using support vector machines. Additionally, experiments are performed by concatenating the Fisher representations of several layers to combine the information of these layers. We will show that our proposed CNN-Fisher approach clearly outperforms other CNN- and non-CNN-based approaches and that our approach requires no training on the target dataset, which results in substantial time savings compared with other CNN-based approaches.(VLID)295911

Adapting fisher vectors for histopathology image classification

Histopathology image classification can provide automated support towards cancer diagnosis. In this paper, we present a transfer learning-based approach for histopathology image classification. We first represent the image feature by Fisher Vector (FV) encoding of local features that are extracted using the Convolutional Neural Network (CNN) model pretrained on ImageNet. Next, to better transfer the pretrained model to the histopathology image dataset, we design a new adaptation layer to further transform the FV descriptors for higher discriminative power and classification accuracy. We used the publicly available BreaKHis image dataset for classifying between benign and malignant breast tumors, and obtained improved performance over the state-of-the-art