7 research outputs found

    Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia

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    In this work, we have concentrated our efforts on the interpretability of classification results coming from a fully convolutional neural network. Motivated by the classification of oesophageal tissue for real-time detection of early squamous neoplasia, the most frequent kind of oesophageal cancer in Asia, we present a new dataset and a novel deep learning method that by means of deep supervision and a newly introduced concept, the embedded Class Activation Map (eCAM), focuses on the interpretability of results as a design constraint of a convolutional network. We present a new approach to visualise attention that aims to give some insights on those areas of the oesophageal tissue that lead a network to conclude that the images belong to a particular class and compare them with those visual features employed by clinicians to produce a clinical diagnosis. In comparison to a baseline method which does not feature deep supervision but provides attention by grafting Class Activation Maps, we improve the F1-score from 87.3% to 92.7% and provide more detailed attention maps

    A clinically interpretable convolutional neural network for the real time prediction of early squamous cell cancer of the esophagus; comparing diagnostic performance with a panel of expert European and Asian endoscopists

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    BACKGROUND AND AIMS: Intrapapillary capillary loops (IPCLs) are microvascular structures that correlate with invasion depth of early squamous cell neoplasia (ESCN) and allow accurate prediction of histology. Artificial intelligence may improve human recognition of IPCL patterns and prediction of histology to allow prompt access to endoscopic therapy of ESCN where appropriate METHODS: One hundred fifteen patients were recruited at 2 academic Taiwanese hospitals. ME-NBI videos of squamous mucosa were labeled as dysplastic or normal according to their histology and IPCL patterns classified by consensus of 3 experienced clinicians. A convolutional neural network (CNN) was trained to classify IPCLs, using 67742 high quality ME-NBI by 5-fold cross validation. Performance measures were calculated to give an average F1 score, accuracy, sensitivity, and specificity. A panel of 5 Asian and 4 European experts predicted the histology of a random selection of 158 images using the JES IPCL classification; accuracy, sensitivity, specificity, positive and negative predictive values were calculated. RESULTS: Expert European Union (EU) and Asian endoscopists attained F1 scores (a measure of binary classification accuracy) of 97.0% and 98%, respectively. Sensitivity and accuracy of the EU and Asian clinicians were 97%, 98% and 96.9%, 97.1% respectively. The CNN average F1 score was 94%, sensitivity 93.7% and accuracy 91.7%. Our CNN operates at video rate and generates class activation maps that can be used to visually validate CNN predictions. CONCLUSIONS: We report a clinically interpretable CNN developed to predict histology based on IPCL patterns, in real-time, using the largest reported dataset of images for this purpose. Our CNN achieved diagnostic performance comparable to an expert panel of endoscopists

    Improving the endoscopic detection of early oesophageal neoplasia

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    The endoscopic detection of oesophageal cancer is complex; largely owing to the subtle appearances of early oesophageal lesions on endoscopy, as well as clinician experience. Early detection is vital, since lesions confined to the mucosal or superficial layers of the submucosa can be treated with endoscopic eradication therapies to good effect. Conversely, patients presenting with late stage oesophageal cancer have very poor outcomes. Improving the detection of oesophageal cancer requires a multifaceted approach. Since the symptoms patients present with are often vague until the disease has progressed beyond the point that it is curable, developing a way to risk stratify or rationalise patient access to endoscopy, based on objective markers of the presence of serious underlying pathology, is vital to allow adequate resource provision in the modern UK endoscopy unit. In patients who do undergo endoscopy there remains a significant mis-rate of cancers in those with de-novo oesophageal cancer as well as those enrolled in Barrett’s oesophagus surveillance programs. We postulate that advanced imaging technologies, in combination with artificial intelligence systems, may improve the diagnostic performance of endoscopists assessing for oesophageal cancers. This body of work presents a comprehensive review of the literature surrounding the epidemiology, detection, classification and endoscopic treatment modalities for both squamous cell and adenocarcinomas of the oesophagus. It also presents four studies undertaken with the overarching aim of improving the endoscopic detection of oesophageal cancer. The first study presents a methodology for the quantification of a biomarker from gastric aspirate samples and an assessment of whether differences in expression levels can be used to predict the presence of neoplasia in patients with or without Barrett’s oesophagus. The second study investigates the role of a novel, advanced endoscopic imaging technology and whether it improves the diagnostic performance of expert and trainee endoscopists assessing Barrett’s oesophagus for the presence of dysplasia or adenocarcinoma. The final two studies present a significant body of work assessing the feasibility and diagnostic performance of a novel artificial intelligence system designed as part of this thesis, for the detection and characterisation of squamous cell cancer of the oesophagus based on microvascular patterns

    Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study

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    BACKGROUND: Intrapapillary capillary loops (IPCLs) represent an endoscopically visible feature of early squamous cell neoplasia (ESCN) which correlate with invasion depth – an important factor in the success of curative endoscopic therapy. IPCLs visualised on magnification endoscopy with Narrow Band Imaging (ME-NBI) can be used to train convolutional neural networks (CNNs) to detect the presence and classify staging of ESCN lesions. METHODS: A total of 7046 sequential high-definition ME-NBI images from 17 patients (10 ESCN, 7 normal) were used to train a CNN. IPCL patterns were classified by three expert endoscopists according to the Japanese Endoscopic Society classification. Normal IPCLs were defined as type A, abnormal as B1–3. Matched histology was obtained for all imaged areas. RESULTS: This CNN differentiates abnormal from normal IPCL patterns with 93.7% accuracy (86.2% to 98.3%) and sensitivity and specificity for classifying abnormal IPCL patterns of 89.3% (78.1% to 100%) and 98% (92% to 99.7%), respectively. Our CNN operates in real time with diagnostic prediction times between 26.17 ms and 37.48 ms. CONCLUSION: Our novel and proof-of-concept application of computer-aided endoscopic diagnosis shows that a CNN can accurately classify IPCL patterns as normal or abnormal. This system could be used as an in vivo, real-time clinical decision support tool for endoscopists assessing and directing local therapy of ESCN
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