A vision transformer-based framework for knowledge transfer from multi-modal to mono-modal lymphoma subtyping models

Determining lymphoma subtypes is a crucial step for better patients treatment targeting to potentially increase their survival chances. In this context, the existing gold standard diagnosis method, which is based on gene expression technology, is highly expensive and time-consuming making difficult its accessibility. Although alternative diagnosis methods based on IHC (immunohistochemistry) technologies exist (recommended by the WHO), they still suffer from similar limitations and are less accurate. WSI (Whole Slide Image) analysis by deep learning models showed promising new directions for cancer diagnosis that would be cheaper and faster than existing alternative methods. In this work, we propose a vision transformer-based framework for distinguishing DLBCL (Diffuse Large B-Cell Lymphoma) cancer subtypes from high-resolution WSIs. To this end, we propose a multi-modal architecture to train a classifier model from various WSI modalities. We then exploit this model through a knowledge distillation mechanism for efficiently driving the learning of a mono-modal classifier. Our experimental study conducted on a dataset of 157 patients shows the promising performance of our mono-modal classification model, outperforming six recent methods from the state-of-the-art dedicated for cancer classification. Moreover, the power-law curve, estimated on our experimental data, shows that our classification model requires a reasonable number of additional patients for its training to potentially reach identical diagnosis accuracy as IHC technologies

Translating AI to digital pathology workflow: Dealing with scarce data and high variation by minimising complexities in data and models

The recent conversion to digital pathology using Whole Slide Images (WSIs) from conventional pathology opened the doors for Artificial Intelligence (AI) in pathology workflow. The recent interests in machine learning and deep learning have gained a high interest in medical image processing. However, WSIs differ from generic medical images. WSIs are complex images which can reveal various information to support different diagnosis varying from cancer to unknown underlying conditions which were not discovered in other medical investigations. These investigations require expert knowledge spending a long time for investigations, applying different stains to the WSIs, and comparing the WSIs. Differences in WSI differentiate general machine learning methods that are applied for medical image processing. Co-analysing multistained WSIs, high variation of the WSIs from different sites, and lack of labelled data are the main key interest areas that directly influence in developing machine learning models that support pathologists in their investigations. However, most of the state-ofthe- art machine learning approaches cannot be applied in the general clinical workflow without using high compute power, expert knowledge, and time. Therefore, this thesis explores avenues to translate the highly computational and time intensive model to a clinical workflow. Co-analysing multi-stained WSIs require registering differently stained WSI together. In order to get a high precision in the registration exploring nonrigid and rigid transformation is required. The non-rigid transformation requires complex deep learning approaches. Using super-convergence on a small Convolutional Neural Network model it is possible to achieve high precision compared to larger auto-encoders and other state-of-the-art models. High variation of the WSIs from different sites heavily effect machine learning models in their predictions. The thesis presents an approach of using a pre-trained model by using only a small number of samples from the new site. Therefore, re-training larger deep learning models are not required which saves expert time for re-labelling and computational power. Finally, lack of labelled data is one of the main issues in training any supervised machine learning or deep learning model. Using a Generative Adversarial Networks (GAN) is an approach which can be easily implemented to avoid this issue. However, GANs are time and computationally expensive. These are not applicable in a general clinical workflow. Therefore, this thesis presents an approach using a simpler GANthat can generate accurate sample labelled data. The synthetic data are used to train classifier and the thesis demonstrates that the predictive model can generate higher accuracy in the test environment. This thesis demonstrates that machine learning and deep learning models can be applied to a clinical workflow, without exploiting expert time and high computing power

Classification of histological images of thyroid nodules based on a combination of Deep Features and Machine Learning

Background: Thyroid nodules are a prevalent worldwide disease with complex pathological types. They can be classified as either benign or malignant. This paper presents a tool for automatically classifying histological images of thyroid nodules, with a focus on papillary carcinoma and follicular adenoma. Methods: In this work, two pre-trained Convolutional Neural Network (CNN) architectures, VGG16 and VGG19, are used to extract deep features. Then, a principal component analysis was used to reduce the dimensionality of the vectors. Then, three machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, and Random Forest) were used for classification. These investigations were applied to our database collection, Results: The proposed investigations have been applied to our private database collection with a total of 112 histological images. The highest results were obtained by the VGG16 transfer deep feature and the SVM classifier with an accuracy rate equal to 100%

The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection

Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight

Deep Domain Adaptation Learning Framework for Associating Image Features to Tumour Gene Profile

While medical imaging and general pathology are routine in cancer diagnosis, genetic sequencing is not always assessable due to the strong phenotypic and genetic heterogeneity of human cancers. Image-genomics integrates medical imaging and genetics to provide a complementary approach to optimise cancer diagnosis by associating tumour imaging traits with clinical data and has demonstrated its potential in identifying imaging surrogates for tumour biomarkers. However, existing image-genomics research has focused on quantifying tumour visual traits according to human understanding, which may not be optimal across different cancer types. The challenge hence lies in the extraction of optimised imaging representations in an objective data-driven manner. Such an approach requires large volumes of annotated image data that are difficult to acquire. We propose a deep domain adaptation learning framework for associating image features to tumour genetic information, exploiting the ability of domain adaptation technique to learn relevant image features from close knowledge domains. Our proposed framework leverages the current state-of-the-art in image object recognition to provide image features to encode subtle variations of tumour phenotypic characteristics with domain adaptation techniques. The proposed framework was evaluated with current state-of-the-art in: (i) tumour histopathology image classification and; (ii) image-genomics associations. The proposed framework demonstrated improved accuracy of tumour classification, as well as providing additional data-derived representations of tumour phenotypic characteristics that exhibit strong image-genomics association. This thesis advances and indicates the potential of image-genomics research to reveal additional imaging surrogates to genetic biomarkers, which has the potential to facilitate cancer diagnosis

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe