scholarly journals Comparison of Deep Transfer Learning Strategies for Digital Pathology

2018 ◽  
Author(s):  
Romain Mormont ◽  
Pierre Geurts ◽  
Raphael Maree
Keyword(s):  
Learning Strategies ◽  
Transfer Learning ◽  
Digital Pathology

Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes

2021 ◽  
Author(s):  
Yun Zhang ◽  
Ling Wang ◽  
Xinqiao Wang ◽  
Chengyun Zhang ◽  
Jiamin Ge ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Deep Learning ◽  
Drug Discovery ◽  
Research And Development ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Chemical Reactions ◽  
Data Augmentation ◽  
Learning Method ◽  
Reaction Prediction

An effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery.

An investigation of aggregated transfer learning for classification in digital pathology

2019 ◽  
Author(s):  
Taranpreet Rai ◽  
Ambra Morisi ◽  
Barbara Bacci ◽  
Nicholas J. Bacon ◽  
Spencer Thomas ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Digital Pathology

scholarly journals Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 213 ◽  
Author(s):  
Ki-Sun Lee ◽  
Jae Young Kim ◽  
Eun-tae Jeon ◽  
Won Suk Choi ◽  
Nan Hee Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Backbone Network ◽  
Deep Learning Algorithm ◽  
Chest X Ray

According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes: COVID-19, pneumonia, and normal. To evaluate the effect of layer depths of the same CNN architecture, CNNs called VGG-16 and VGG-19 were used as backbone networks. Then, each backbone network was trained with different degrees of fine-tuning and comparatively evaluated. The experimental results showed the highest AUC value to be 0.950 concerning COVID-19 classification in the experimental group of a fine-tuned with only 2/5 blocks of the VGG16 backbone network. In conclusion, in the classification of medical images with a limited number of data, a deeper layer depth may not guarantee better results. In addition, even if the same pre-trained CNN architecture is used, an appropriate degree of fine-tuning can help to build an efficient deep learning model.

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

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 256
Author(s):  
Francesco Ponzio ◽  
Gianvito Urgese ◽  
Elisa Ficarra ◽  
Santa Di Cataldo
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Digital Pathology ◽  
Training Data ◽  
Automated Classification ◽  
Computationally Efficient ◽  
Deep Convolutional Neural Networks ◽  
Cad Systems ◽  
Tissue Characteristics

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.

Deep Transfer Learning for Histopathological Diagnosis of Cervical Cancer Using Convolutional Neural Networks with Visualization Schemes

2020 ◽  
Vol 10 (2) ◽  
pp. 391-400 ◽  
Author(s):  
Ying Chen ◽  
Xiaomin Qin ◽  
Jingyu Xiong ◽  
Shugong Xu ◽  
Jun Shi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cervical Cancer ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Characteristic Curve ◽  
Disease Classification ◽  
Natural Image ◽  
Histopathological Diagnosis ◽  
The Impact

This study aimed to propose a deep transfer learning framework for histopathological image analysis by using convolutional neural networks (CNNs) with visualization schemes, and to evaluate its usage for automated and interpretable diagnosis of cervical cancer. First, in order to examine the potential of the transfer learning for classifying cervix histopathological images, we pre-trained three state-of-the-art CNN architectures on large-size natural image datasets and then fine-tuned them on small-size histopathological datasets. Second, we investigated the impact of three learning strategies on classification accuracy. Third, we visualized both the multiple-layer convolutional kernels of CNNs and the regions of interest so as to increase the clinical interpretability of the networks. Our method was evaluated on a database of 4993 cervical histological images (2503 benign and 2490 malignant). The experimental results demonstrated that our method achieved 95.88% sensitivity, 98.93% specificity, 97.42% accuracy, 94.81% Youden's index and 99.71% area under the receiver operating characteristic curve. Our method can reduce the cognitive burden on pathologists for cervical disease classification and improve their diagnostic efficiency and accuracy. It may be potentially used in clinical routine for histopathological diagnosis of cervical cancer.

scholarly journals Towards CNN Representations for Small Mass Spectrometry Data Classification: From Transfer Learning to Cumulative Learning

2020 ◽  
Author(s):  
Khawla Seddiki ◽  
Philippe Saudemont ◽  
Frédéric Precioso ◽  
Nina Ogrinc ◽  
Maxence Wisztorski ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Data Classification ◽  
Representation Learning ◽  
Mass Spectrometry Data ◽  
Training Dataset ◽  
Require Time ◽  
Training Samples

AbstractRapid and accurate clinical diagnosis of pathological conditions remains highly challenging. A very important component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some popular Machine Learning (ML) approaches have been investigated for this purpose but these ML models require time-consuming preprocessing steps such as baseline correction, denoising, and spectrum alignment to remove non-sample-related data artifacts. They also depend on the tedious extraction of handcrafted features, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn efficient representations from raw data without the need for costly preprocessing. However, their effectiveness drastically decreases when the number of available training samples is small, which is a common situation in medical applications. Transfer learning strategies extend an accurate representation model learnt usually on a large dataset containing many categories, to a smaller dataset with far fewer categories. In this study, we first investigate transfer learning on a 1D-CNN we have designed to classify MS data, then we develop a new representation learning method when transfer learning is not powerful enough, as in cases of low-resolution or data heterogeneity. What we propose is to train the same model through several classification tasks over various small datasets in order to accumulate generic knowledge of what MS data are, in the resulting representation. By using rat brain data as the initial training dataset, a representation learning approach can have a classification accuracy exceeding 98% for canine sarcoma cancer cells, human ovarian cancer serums, and pathogenic microorganism biotypes in 1D clinical datasets. We show for the first time the use of cumulative representation learning using datasets generated in different biological contexts, on different organisms, in different mass ranges, with different MS ionization sources, and acquired by different instruments at different resolutions. Our approach thus proposes a promising strategy for improving MS data classification accuracy when only small numbers of samples are available as a prospective cohort. The principles demonstrated in this work could even be beneficial to other domains (astronomy, archaeology…) where training samples are scarce.

scholarly journals Transfer Learning Strategies for Anomaly Detection in IoT Vibration Data

2021 ◽  
Author(s):  
Clemens Heistracher ◽  
Anahid Jalali ◽  
Indu Strobl ◽  
Axel Suendermann ◽  
Sebastian Meixner ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Training Data ◽  
Sensor Data ◽  
Classification Models ◽  
Vibration Data ◽  
Starting Point ◽  
Wide Range ◽  
Anomaly Classification

<div>Abstract—An increasing number of industrial assets are equipped with IoT sensor platforms and the industry now expects data-driven maintenance strategies with minimal deployment costs. However, gathering labeled training data for supervised tasks such as anomaly detection is costly and often difficult to implement in operational environments. Therefore, this work aims to design and implement a solution that reduces the required amount of data for training anomaly classification models on time series sensor data and thereby brings down the overall deployment effort of IoT anomaly detection sensors. We set up several in-lab experiments using three peristaltic pumps and investigated approaches for transferring trained anomaly detection models across assets of the same type. Our experiments achieved promising effectiveness and provide initial evidence that transfer learning could be a suitable strategy for using pretrained anomaly classification models across industrial assets of the same type with minimal prior labeling and training effort. This work could serve as a starting point for more general, pretrained sensor data embeddings, applicable to a wide range of assets.</div>

scholarly journals Damage detection using in-domain and cross-domain transfer learning

2021 ◽  
Author(s):  
Zaharah A. Bukhsh ◽  
Nils Jansen ◽  
Aaqib Saeed
Keyword(s):  
Damage Detection ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Large Scale ◽  
Visual Inspection ◽  
Superior Performance ◽  
Cross Domain ◽  
Domain Transfer ◽  
Domain Transfer Learning ◽  
The Impact

AbstractWe investigate the capabilities of transfer learning in the area of structural health monitoring. In particular, we are interested in damage detection for concrete structures. Typical image datasets for such problems are relatively small, calling for the transfer of learned representation from a related large-scale dataset. Past efforts of damage detection using images have mainly considered cross-domain transfer learning approaches using pre-trained ImageNet models that are subsequently fine-tuned for the target task. However, there are rising concerns about the generalizability of ImageNet representations for specific target domains, such as for visual inspection and medical imaging. We, therefore, evaluate a combination of in-domain and cross-domain transfer learning strategies for damage detection in bridges. We perform comprehensive comparisons to study the impact of cross-domain and in-domain transfer, with various initialization strategies, using six publicly available visual inspection datasets. The pre-trained models are also evaluated for their ability to cope with the extremely low-data regime. We show that the combination of cross-domain and in-domain transfer persistently shows superior performance specially with tiny datasets. Likewise, we also provide visual explanations of predictive models to enable algorithmic transparency and provide insights to experts about the intrinsic decision logic of typically black-box deep models.

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