Transfer Learning for Colonic Polyp Classification Using Off-the-Shelf CNN Features

Ribeiro, Eraldo; Uhl, Andreas; Wimmer, Georg; Häfner, Michael

doi:10.1007/978-3-319-54057-3_1

Cited by 8 publications

(8 citation statements)

References 31 publications

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“…Their results imply that CNNs are suitable for computer aided diagnosis problems, and transfer learning from large-scale annotated natural image datasets is beneficial for performance (which according to our preliminary studies does not apply to the problem of scene classification). For colonic polyp classification, Riberio et al [21] proposed transfer learning using off-the-shelf CNN features. Based on high-level CNN features (from CNNs trained for object recognition), Ng et al [4] use semantic fisher vectors for semantic classification of natural video scenes.…”

Section: Related Workmentioning

confidence: 99%

Learning laparoscopic video shot classification for gynecological surgery

Petscharnig

Schöffmann

2017

Multimed Tools Appl

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Videos of endoscopic surgery are used for education of medical experts, analysis in medical research, and documentation for everyday clinical life. Hand-crafted image descriptors lack the capabilities of a semantic classification of surgical actions and video shots of anatomical structures. In this work, we investigate how well single-frame convolutional neural networks (CNN) for semantic shot classification in gynecologic surgery work. Together with medical experts, we manually annotate hours of raw endoscopic gynecologic surgery videos showing endometriosis treatment and myoma resection of over 100 patients. The cleaned ground truth dataset comprises 9 h of annotated video material (from 111 different recordings). We use the well-known CNN architectures AlexNet and GoogLeNet and train these architectures for both, surgical actions and anatomy, from scratch. Furthermore, we extract high-level features from AlexNet with weights from a pre-trained model from the Caffe model zoo and feed them to an SVM classifier. Our evaluation shows that we reach an average recall of .697 and .515 for classification of anatomical structures and surgical actions respectively using off-the-shelf CNN features. Using GoogLeNet, we achieve a mean recall of .782 and .617 for classification of anatomical structures and surgical actions respectively. With AlexNet the achieved recall is .615 for anatomical structures and .469 for surgical action classification respectively. The main conclusion of our work is that advances in general image classification methods transfer to the domain of endoscopic surgery videos in gynecology. This is relevant as this domain is different from natural images, e.g. it is distinguished by smoke, reflections, or a limited amount of colors.

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Section: Related Workmentioning

confidence: 99%

Learning laparoscopic video shot classification for gynecological surgery

Petscharnig

Schöffmann

2017

Multimed Tools Appl

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“…Transfer learning reduces the time and computational resources needed to train deep neural networks from scratch on a large amount of data because it does not need to take time optimizing millions of parameters. Despite the disparity between natural images and biological images, deep CNN architectures comprehensively trained on the large-scale well-annotated ImageNet can still be transferred to biological and medical domains and have demonstrated their successes in various image classification tasks, such as skin cancer, 37 colon polyps, 51 thyroid nodules, 52 thoraco-abdominal lymph nodes, and interstitial lung disease 53 . The GoogleNet Inception v3 model consists of 22 layers stacked on top of each other (see Fig.…”

Section: Methodsmentioning

confidence: 99%

Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer

Weng

et al. 2017

J. Biomed. Opt

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Abstract. Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on human lung tissues. However, conventional means of analyzing CARS images requires extensive image processing, feature engineering, and human intervention. This study demonstrates the feasibility of applying a deep learning algorithm to automatically differentiate normal and cancerous lung tissue images acquired by CARS. We leverage the features learned by pretrained deep neural networks and retrain the model using CARS images as the input. We achieve 89.2% accuracy in classifying normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma lung images. This computational method is a step toward on-the-spot diagnosis of lung cancer and can be further strengthened by the efforts aimed at miniaturizing the CARS technique for fiber-based microendoscopic imaging. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Since our data is not large enough to train a CNN model from scratch, we used transfer learning because of its outstanding performance in the computer vision domain in general and in the medical data domain in specific [30]- [35]. We fine-tuned different models as a base model and then added a dense layer to reflect our five-class classification problem.…”

Section: B Problem Formulationmentioning

confidence: 99%

Fine-Tuning Pre-Trained Convolutional Neural Networks for Women Common Cancer Classification using RNA-Seq Gene Expression

Alharbi¹,

Elbashir²,

Mohammed³

et al. 2020

IJACSA

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Most of the recent cancer classification methods use gene expression profile as features because it can provide very important information regarding tumor characteristics. Motivated by their success in the computer vision area now deep learning has been successfully applied to medical data because it can read non-linear patterns in a complex feature and can allow the leverage of information from unlabeled data of problems that do not belong to the problem being handled. In this paper, we implement transfer learning, which refers to the use of a model trained on one task to perform classification on another task to classify five cancer types that most commonly affect women. We used VGG16, Xception, DenseNet, and ResNet50 as base models and then added a dense layer to reflect our five-class classification problem. To avoid training over-fitting that can result in a very high training accuracy and a low cross-validation accuracy we used L2-regularization. We retrained (fine-tuned) these models using a five-fold cross-validation approach on RNA-Seq gene expression data after transforming it into 2D-image like data. We used the softmax activation function with the prediction dense layer and adam as optimizer in the model fit for all four architectures. The highest performance is obtained when fine-tuning Xception architecture, which achieved classification accuracy = 98.6%, precision = 98.6%, recall = 97.8%, and F1score = 98% on five-fold cross-validation training and testing approach.

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Transfer Learning for Colonic Polyp Classification Using Off-the-Shelf CNN Features

Cited by 8 publications

References 31 publications

Learning laparoscopic video shot classification for gynecological surgery

Learning laparoscopic video shot classification for gynecological surgery

Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer

Fine-Tuning Pre-Trained Convolutional Neural Networks for Women Common Cancer Classification using RNA-Seq Gene Expression

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