Convolutional Neural Network–Based Automatic Classification of Colorectal and Prostate Tumor Biopsies Using Multispectral Imagery: System Development Study

Background: Colorectal and prostate cancers are the most common types of cancer in men worldwide. To diagnose colorectal and prostate cancer, a pathologist performs a histological analysis on needle biopsy samples. This manual process is time-consuming and error-prone, resulting in high intra- and interobserver variability, which affects diagnosis reliability. Objective: This study aims to develop an automatic computerized system for diagnosing colorectal and prostate tumors by using images of biopsy samples to reduce time and diagnosis error rates associated with human analysis. Methods: In this study, we proposed a convolutional neural network (CNN) model for classifying colorectal and prostate tumors from multispectral images of biopsy samples. The key idea was to remove the last block of the convolutional layers and halve the number of filters per layer. Results: Our results showed excellent performance, with an average test accuracy of 99.8% and 99.5% for the prostate and colorectal data sets, respectively. The system showed excellent performance when compared with pretrained CNNs and other classification methods, as it avoids the preprocessing phase while using a single CNN model for the whole classification task. Overall, the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images. Conclusions: The proposed CNN architecture was detailed and compared with previously trained network models used as feature extractors. These CNNs were also compared with other classification techniques. As opposed to pretrained CNNs and other classification approaches, the proposed CNN yielded excellent results. The computational complexity of the CNNs was also investigated, and it was shown that the proposed CNN is better at classifying images than pretrained networks because it does not require preprocessing. Thus, the overall analysis was that the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images

Automated classification of cancer tissues using multispectral imagery

Automated classification of medical images for colorectal and prostate cancer diagnosis is a crucial tool for improving routine diagnosis decisions. Therefore, in the last few decades, there has been an increasing interest in refining and adapting machine learning algorithms to classify microscopic images of tumour biopsies. Recently, multispectral imagery has received a significant interest from the research community due to the fast-growing development of high-performance computers. This thesis investigates novel algorithms for automatic classification of colorectal and prostate cancer using multispectral imagery in order to propose a system outperforming the state-of-the-art techniques in the field. To achieve this objective, several feature extraction methods based on image texture have been investigated, analysed and evaluated. A novel texture feature for multispectral images is also constructed as an adaptation of the local binary pattern texture feature to multispectral images by expanding the pixels neighbourhood to the spectral dimension. It has the advantage of capturing the multispectral information with a limited feature vector size. This feature has demonstrated improved classification results when compared against traditional texture features. In order to further enhance the systems performance, advanced classification schemes such as bag-of-features - to better capture local information - and stacked generalisation - to select the most discriminative texture features - are explored and evaluated. Finally, the recent years have seen an accelerated and exponential rise of deep learning, boosted by the advances in hardware, and more specifically graphics processing units. Such models have demonstrated excellent results for supervised learning in multiple applications. This observation has motivated the employment in this thesis of deep neural network architectures, namely convolutional neural networks. Experiments were also carried out to evaluate and compare the performance obtained with the features extracted using convolutional neural networks with random initialisation against features extracted with pre-trained models on ImageNet dataset. The analysis of the classication accuracy achieved with deep learning models reveals that the latter outperforms the previously proposed texture extraction methods. In this thesis, the algorithms are assessed using two separate multiclass datasets: the first one consists of prostate tumour multispectral images, and the second contains multispectral images of colorectal tumours. The colorectal dataset was acquired on a wide domain of the light spectrum ranging from the visible to the infrared wavelengths. This dataset was used to demonstrate the improved results produced using infrared light as well as visible light

Technological Advances in the Diagnosis and Management of Pigmented Fundus Tumours

Choroidal naevi are the most common intraocular tumour. They can be pigmented or non-pigmented and have a predilection for the posterior uvea. The majority remain undetected and cause no harm but are increasingly found on routine community optometry examinations. Rarely does a naevus demonstrate growth or the onset of suspicious features to fulfil the criteria for a malignant melanoma. Because of this very small risk, optometrists commonly refer these patients to hospital eye units for a second opinion, triggering specialist examination and investigation, causing significant anxiety to patients and stretching medical resources. This PhD thesis introduces the MOLES acronym and scoring system that has been devised to categorise the risk of malignancy in choroidal melanocytic tumours according to Mushroom tumour shape, Orange pigment, Large tumour size, Enlarging tumour and Subretinal fluid. This is a simplified system that can be used without sophisticated imaging, and hence its main utility lies in the screening of patients with choroidal pigmented lesions in the community and general ophthalmology clinics. Under this system, lesions were categorised by a scoring system as ‘common naevus’, ‘low-risk naevus’, ‘high-risk naevus’ and ‘probable melanoma.’ According to the sum total of the scores, the MOLES system correlates well with ocular oncologists’ final diagnosis. The PhD thesis also describes a model of managing such lesions in a virtual pathway, showing that images of choroidal naevi evaluated remotely using a decision-making algorithm by masked non-medical graders or masked ophthalmologists is safe. This work prospectively validates a virtual naevus clinic model focusing on patient safety as the primary consideration. The idea of a virtual naevus clinic as a fast, one-stop, streamlined and comprehensive service is attractive for patients and healthcare systems, including an optimised patient experience with reduced delays and inconvenience from repeated visits. A safe, standardised model ensures homogeneous management of cases, appropriate and prompt return of care closer to home to community-based optometrists. This research work and strategies, such as the MOLES scoring system for triage, could empower community-based providers to deliver management of benign choroidal naevi without referral to specialist units. Based on the positive outcome of this prospective study and the MOLES studies, a ‘Virtual Naevus Clinic’ has been designed and adapted at Moorfields Eye Hospital (MEH) to prove its feasibility as a response to the COVID-19 pandemic, and with the purpose of reducing in-hospital patient journey times and increasing the capacity of the naevus clinics, while providing safe and efficient clinical care for patients. This PhD chapter describes the design, pathways, and operating procedures for the digitally enabled naevus clinics in Moorfields Eye Hospital, including what this service provides and how it will be delivered and supported. The author will share the current experience and future plan. Finally, the PhD thesis will cover a chapter that discusses the potential role of artificial intelligence (AI) in differentiating benign choroidal naevus from choroidal melanoma. The published clinical and imaging risk factors for malignant transformation of choroidal naevus will be reviewed in the context of how AI applied to existing ophthalmic imaging systems might be able to determine features on medical images in an automated way. The thesis will include current knowledge to date and describe potential benefits, limitations and key issues that could arise with this technology in the ophthalmic field. Regulatory concerns will be addressed with possible solutions on how AI could be implemented in clinical practice and embedded into existing imaging technology with the potential to improve patient care and the diagnostic process. The PhD will also explore the feasibility of developed automated deep learning models and investigate the performance of these models in diagnosing choroidal naevomelanocytic lesions based on medical imaging, including colour fundus and autofluorescence fundus photographs. This research aimed to determine the sensitivity and specificity of an automated deep learning algorithm used for binary classification to differentiate choroidal melanomas from choroidal naevi and prove that a differentiation concept utilising a machine learning algorithm is feasible

34th Annual Meeting & Pre-Conference Programs of the Society for Immunotherapy of Cancer (SITC 2019): part 2

Document type: Articl

Multispectral biopsy image based colorectal tumor grader

Automated tumor cell grading systems have an immense potential in improving the speed and accuracy of cancer diagnostic procedures. It can boost the confidence level of pathologists who perform the manual assessment of tumor cells. The application of image processing and machine learning techniques on the digitized biopsy slides enables the discrimination between various cell types. Deployment of multispectral imaging technique for biopsy slide digitization serves to provide spectral information along with the spatial information. Multispectral imaging allows to acquire several images of the sample in multiple wavelengths including the infrared ranges. This paper presents a multispectral image based colorectal tumor grading system. The algorithm validation is performed on our biopsy image database comprising 200 samples from 4 classes, viz. normal, hyperplastic polyp, tubular adenoma low grade as well as carcinoma cells. In addition to the visible bands, we have incorporated the spectral bands in near infrared ranges. Rotation invariant Local phase quantization (LPQ) feature extraction on our multispectral images have yielded a classification accuracy of 86.05% with an SVM classifier. Moreover, the experiments were carried out on another small multispectral image dataset which had 3 categories of cells.This publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 6-249-1-053. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.Scopu