Machine learning methods for histopathological image analysis

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.Comment: 23 pages, 4 figure

Deep Learning Solutions for Lung Cancer Characterization in Histopathological Images

Cancer is one of the leading death causes in the world, specifically, lung cancer. According to theWorld Health Organization (WHO), at the end of 2020, around 2.2 million people were diagnosedwith lung cancer, and 1.8 million fatalities resulted from it. Correctly identifying it's presence in apatient and classifying it's sub-type and stage is fundamental for the adoption of appropriate targettherapies. One of the gold standards used to identify and classify cancer is the microscopic visual in-spection of histopathological imagesi.e.small tissue samples excised from a patient. Expertpathologists are responsible for this inspection, however, it requires a significant amount of timeand sometimes leads to non-consensual results . With the growth of computational power and data availability, modern Artificial Intelligencesolutions can be developed to automate and speed up this process. Deep Neural Networks us-ing histopathological images as an input currently embody the state-of-the-art in automated lungcancer diagnostic solutions, with Deep Convolutional Neural Networks achieving the most com-pelling acuracies in tissue type classification. One of the main reasons for such results is theincreasing availability of voluminous amounts of data, acquired through the efforts employed byextensive projects like The Cancer Genome Atlas. Nonetheless, histopathological images remain weakly labelled/annotated, as most commonpathologist annotations refer to the entirety of the image and not to individual regions of interestin the patient's tissue sample. Recent works have demonstrated Multiple Instance Learning as asuccessful approach in classification tasks entangled with this lack of annotation, by representingimages as a bag of instances where a single label is available for the whole bag. Thus, we propose a bag/embedding-level lung tissue type and sub-type classifier using a Con-volutional Neural Network in a Multiple Instance Learning approach, where the automated inspec-tion of lung histopathological images determines the presence of cancer, and it's possible sub-type,in a given patient. Furthermore, we employ a post-model interpretability algorithm to validate ourmodel's predictions and highlight the regions of interest for such predictions

GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis

Histopathological cancer diagnosis is based on visual examination of stained tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely employed worldwide. It is easy to acquire and cost effective, but cells and tissue components show low-contrast with varying tones of dark blue and pink, which makes difficult visual assessments, digital image analysis, and quantifications. These limitations can be overcome by IHC staining of target proteins of the tissue slide. IHC provides a selective, high-contrast imaging of cells and tissue components, but their use is largely limited by a significantly more complex laboratory processing and high cost. We proposed a conditional CycleGAN (cCGAN) network to transform the H\&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide. This data-driven method requires only a limited amount of labelled data but will generate pixel level segmentation results. The proposed cCGAN model improves the original network \cite{zhu_unpaired_2017} by adding category conditions and introducing two structural loss functions, which realize a multi-subdomain translation and improve the translation accuracy as well. % need to give reasons here. Experiments demonstrate that the proposed model outperforms the original method in unpaired image translation with multi-subdomains. We also explore the potential of unpaired images to image translation method applied on other histology images related tasks with different staining techniques

A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model

Deep learning algorithms have yielded remarkable results in medical diagnosis and image analysis, besides their contribution to improvements in a number of fields such as drug discovery, time-series modelling and optimisation methods. With regard to the analysis of histopathologic breast cancer images, the similarity of those images and the presence of healthy and tumourous tissues in different areas complicate the detection and classification of tumours on whole slide images. An accurate diagnosis in a short time is a need for full treatment in breast cancer. A successful classification on breast cancer histopathological images will overcome the burden on the pathologist and reduce the subjectivity of diagnosis. In this study, we propose a deep convolutional neural network model. The model uses various algorithms (i.e., stochastic gradient descent, Nesterov accelerated gradient, adaptive gradient, RMSprop, AdaDelta and Adam) to compute the initial weight of the network and update the model parameters for faster backpropagation learning. In order to train the model with less hardware in a short time, we used the parallel computing architecture with Cuda-enabled graphics processing unit. The results indicate that the deep convolutional neural network model is an effective classification model with a high performance up to 99.05% accuracy value. © 2020, Springer Science+Business Media, LLC, part of Springer Nature

Color and morphological features extraction and nuclei classification in tissue samples of colorectal cancer

Cancer is an important public health problem and the third most leading cause of death in North America. Among the highest impact types of cancer are colorectal, breast, lung, and prostate. This thesis addresses the features extraction by using different artificial intelligence algorithms that provide distinct solutions for the purpose of Computer-AidedDiagnosis (CAD). For example, classification algorithms are employed in identifying histological structures, such as lymphocytes, cancer-cells nuclei and glands, from features like existence, extension or shape. The morphological aspect of these structures indicates the degree of severity of the related disease. In this paper, we use a large dataset of 5000 images to classify eight different tissue types in the case of colorectal cancer. We compare results with another dataset. We perform image segmentation and extract statistical information about the area, perimeter, circularity, eccentricity and solidity of the interest points in the image. Finally, we use and compare four popular machine learning techniques, i.e., Naive Bayes, Random Forest, Support Vector Machine and Multilayer Perceptron to classify and to improve the precision of category assignation. The performance of each algorithm was measured using 3 types of metrics: Precision, recall and F1-Score representing a huge contribution to the existing literature complementing it in a quantitative way. The large number of images has helped us to circumvent the overfitting and reproducibility problems. The main contribution is the use of new characteristics different from those already studied, this work researches about the color and morphological characteristics in the images that may be useful for performing tissue classification in colorectal cancer histology

Fast ScanNet : fast and dense analysis of multi-gigapixel whole-slide images for cancer metastasis detection

Lymph node metastasis is one of the most important indicators in breast cancer diagnosis, that is traditionally observed under the microscope by pathologists. In recent years, with the dramatic advance of high-throughput scanning and deep learning technology, automatic analysis of histology from whole- slide images has received a wealth of interest in the field of medical image computing, which aims to alleviate pathologists’ workload and simultaneously reduce misdiagnosis rate. However, automatic detection of lymph node metastases from whole-slide images remains a key challenge because such images are typically very large, where they can often be multiple gigabytes in size. Also, the presence of hard mimics may result in a large number of false positives. In this paper, we propose a novel method with anchor layers for model conversion, which not only leverages the efficiency of fully convolutional architectures to meet the speed requirement in clinical practice, but also densely scans the whole- slide image to achieve accurate predictions on both micro- and macro-metastases. Incorporating the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. The efficacy of our method are corroborated on the benchmark dataset of 2016 Camelyon Grand Challenge. Our method achieved significant improvements in comparison with the state-of-the-art methods on tumour localization accuracy with a much faster speed and even surpassed human performance on both challenge tasks