Colorectal Cancer Classification using Deep Convolutional Networks - An Experimental Study

Ponzio, Francesco; Macii, Enrico; Ficarra, Elisa; Cataldo, Santa Di

doi:10.5220/0006643100580066

Cited by 47 publications

(30 citation statements)

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“…Our previous work on colorectal image analysis targeted a simplified classification problem where adenomas were treated as a unicum, overlooking their specific sub-classes [16]. In this paper, we address the full histological assessment of colorectal polyps (malignant, non-malignant, and cancer precursors), considering five main histological categories: For our study, we obtained 41 hematoxylin and eosin colon tissue slides from the Virtual Pathology Slide Library of the University of Leeds, a repository of histological samples that have been digitized and curated by a trained pathologist.…”

Section: Colorectal Polyps Assessmentmentioning

confidence: 99%

“…A very preliminary version of this study was recently presented in a conference paper, targeting the specific problem of colorectal image classification [16]. In the current paper, we address the problem of histological image classification from a general point of view, using colorectal polyps assessment, which is an important and challenging problem in histopathology and medicine, just as a case study.…”

Section: Introductionmentioning

confidence: 99%

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Dealing with Lack of Training Data for Convolutional Neural Networks: The Case of Digital Pathology

et al. 2019

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Thanks to their capability to learn generalizable descriptors directly from images, deep Convolutional Neural Networks (CNNs) seem the ideal solution to most pattern recognition problems. On the other hand, to learn the image representation, CNNs need huge sets of annotated samples that are unfeasible in many every-day scenarios. This is the case, for example, of Computer-Aided Diagnosis (CAD) systems for digital pathology, where additional challenges are posed by the high variability of the cancerous tissue characteristics. In our experiments, state-of-the-art CNNs trained from scratch on histological images were less accurate and less robust to variability than a traditional machine learning framework, highlighting all the issues of fully training deep networks with limited data from real patients. To solve this problem, we designed and compared three transfer learning frameworks, leveraging CNNs pre-trained on non-medical images. This approach obtained very high accuracy, requiring much less computational resource for the training. Our findings demonstrate that transfer learning is a solution to the automated classification of histological samples and solves the problem of designing accurate and computationally-efficient CAD systems with limited training data.

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Section: Colorectal Polyps Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dealing with Lack of Training Data for Convolutional Neural Networks: The Case of Digital Pathology

et al. 2019

Self Cite

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“…Intestine images taken under the white light source are not different from the images seen by the human eye. The other light source is the NBI light source, also known as the endoscope [38]. It uses a filter to filter the white light of the endoscope light source, and only retains the narrow-band spectrum, so it is also called narrow-band endoscopic imaging.…”

Section: Figure 2data Screening Processmentioning

confidence: 99%

Application of Massive Parallel Deep Learning Algorithm in the Prediction of Colorectal Carcinogenesis of Familial Polyposis

Zhang

Jiang

2020

IEEE Access

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Based on the massively parallel deep learning algorithm, this paper studies familial polyposis colorectal carcinogenesis, and proposes a semi-supervised multi-task survival analysis method based on deep learning, which transforms the survival analysis problem into multi-timepoint survival probability prediction. The multi-task learning model is composed of semi-supervised learning problems. We use semi-supervised loss and sorting loss to deal with data of censorship and the non-increasing probability of survival probability. It established a prognostic risk prediction model for familial polyposis colorectal cancer based on a semisupervised logistic regression method and learns from supervised learning from five aspects of discriminating ability, interpretability, and clinical practicality. The method comparison expands the current understanding of the generalization capabilities of different models and provides a reference for the establishment of clinical prediction models. The effectiveness of this method was verified by external data and provided technical support for constructing a prognostic model with application value for multi-center real clinical data. This model has demonstrated better prediction performance than common models in the prognostic task of familial polyposis colorectal cancer, and successfully described the mechanism of action of predictors. INDEX TERMS massively parallel computing; deep learning; familial polyposis colorectal cancer; predictive application.

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“…Colorectal cancer (CRC) is a form of cancer that occurs globally and is one of the most common forms of cancer among both men and women in terms of the causes of human mortality [1,2]. Recently, reports have identified that the number of people with CRC younger than 50 years old is increasing, which means cancer screening is a more essential process than ever [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Utilizing tissue image datasets, [2] developed an experimental CNN with transfer learning and fine-tuning for histology in CRC diagnosis, in which the CNN provided good testing classification accuracy up to 90%. In [37], large image sizes were applied with CNN and evaluated for colorectal cancer grading classification, achieving accuracy for two classes of 99.28% and three classes of 95.70%.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening

Jinsakul

Tsai

et al. 2019

Mathematics

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One of the leading forms of cancer is colorectal cancer (CRC), which is responsible for increasing mortality in young people. The aim of this paper is to provide an experimental modification of deep learning of Xception with Swish and assess the possibility of developing a preliminary colorectal polyp screening system by training the proposed model with a colorectal topogram dataset in two and three classes. The results indicate that the proposed model can enhance the original convolutional neural network model with evaluation classification performance by achieving accuracy of up to 98.99% for classifying into two classes and 91.48% for three classes. For testing of the model with another external image, the proposed method can also improve the prediction compared to the traditional method, with 99.63% accuracy for true prediction of two classes and 80.95% accuracy for true prediction of three classes.

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Colorectal Cancer Classification using Deep Convolutional Networks - An Experimental Study

Cited by 47 publications

References 0 publications

Dealing with Lack of Training Data for Convolutional Neural Networks: The Case of Digital Pathology

Dealing with Lack of Training Data for Convolutional Neural Networks: The Case of Digital Pathology

Application of Massive Parallel Deep Learning Algorithm in the Prediction of Colorectal Carcinogenesis of Familial Polyposis

Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening

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