Masked convolutional neural network for supervised learning problems

Liu, Leo Yu-Feng; Liu, Yufeng; Zhu, Hongtu

doi:10.1002/sta4.290

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…39,40 However, when tasks explicitly depend on the absolute position of pixels, the recovery of spatialized information may not be sufficiently accurate. 36 For mPI-to-mPD conversion of flattened beams, the spatialized information relates to the off -axis dose modulation due to both the incident fluence modulation, mainly caused by the flattening filter, and the difference in spectral energy response between water and a-Si of the EPID. 37 In addition, the Flood Field correction applied on the mPIs flattens the EPID signal.…”

Section: True Dose Modulation Layermentioning

confidence: 99%

“…To address this in the present deep-learning approach, different learning-based solutions were explored such as densely connected, 2D locally connected and 2D CoordConv layer proposed by Liu et al 36 However, these layers must be optimized in a time-consuming learning process, and they did not provide sufficient model convergence. A simpler and more modular solution was chosen.…”

Section: True Dose Modulation Layermentioning

confidence: 99%

“…However, the use of CNNs can result in insufficient encoding of spatialized information. 36 This can be an obstacle for mPI-to-mPD conversion where pixel-wise transformations, such as beam profile restoration, 37 are required.…”

Section: Introductionmentioning

confidence: 99%

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A convolutional neural network model for EPID‐based non‐transit dosimetry

Bosco

Franceries

Romain³

et al. 2023

J Applied Clin Med Phys

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Purpose To develop an alternative computational approach for EPID‐based non‐transit dosimetry using a convolutional neural network model. Method A U‐net followed by a non‐trainable layer named True Dose Modulation recovering the spatialized information was developed. The model was trained on 186 Intensity‐Modulated Radiation Therapy Step & Shot beams from 36 treatment plans of different tumor locations to convert grayscale portal images into planar absolute dose distributions. Input data were acquired from an amorphous‐Silicon Electronic Portal Image Device and a 6 MV X‐ray beam. Ground truths were computed from a conventional kernel‐based dose algorithm. The model was trained by a two‐step learning process and validated through a five‐fold cross‐validation procedure with sets of training and validation of 80% and 20%, respectively. A study regarding the dependance of the amount of training data was conducted. The performance of the model was evaluated from a quantitative analysis based the ϒ‐index, absolute and relative errors computed between the inferred dose distributions and ground truths for six square and 29 clinical beams from seven treatment plans. These results were also compared to those of an existing portal image‐to‐dose conversion algorithm. Results For the clinical beams, averages of ϒ‐index and ϒ‐passing rate (2%‐2mm > 10% Dmax) of 0.24 (±0.04) and 99.29 (±0.70)% were obtained. For the same metrics and criteria, averages of 0.31 (±0.16) and 98.83 (±2.40)% were obtained with the six square beams. Overall, the developed model performed better than the existing analytical method. The study also showed that sufficient model accuracy can be achieved with the amount of training samples used. Conclusion A deep learning‐based model was developed to convert portal images into absolute dose distributions. The accuracy obtained shows that this method has great potential for EPID‐based non‐transit dosimetry.

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Section: True Dose Modulation Layermentioning

confidence: 99%

Section: True Dose Modulation Layermentioning

confidence: 99%

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A convolutional neural network model for EPID‐based non‐transit dosimetry

Bosco

Franceries

Romain³

et al. 2023

J Applied Clin Med Phys

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“…Mask-based convolutional neural networks (MCNNs) are a class of CNNs used to avoid background noise, and have been applied to person retrieval [29]. In MCNNs, a latent binary mapping of the raw data is first learned by a specific neural network, such as the fully convolutional network [29] or the U-net [30]. The learned latent binary mapping extracts regions of interest from the raw data that contain informative signals for subsequent tasks, which is similar to the prior knowledge defined in this study.…”

Section: Mask-based Convolutional Neural Networkmentioning

confidence: 99%

Improving Accuracy and Interpretability of CNN-Based Fault Diagnosis through an Attention Mechanism

Huang,

Zhang,

Liu

et al. 2023

Processes

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This study aims to enhance the accuracy and interpretability of fault diagnosis. To address this objective, we present a novel attention-based CNN method that leverages image-like data generated from multivariate time series using a sliding window processing technique. By representing time series data in an image-like format, the spatiotemporal dependencies inherent in the raw data are effectively captured, which allows CNNs to extract more comprehensive fault features, consequently enhancing the accuracy of fault diagnosis. Moreover, the proposed method incorporates a form of prior knowledge concerning category-attribute correlations into CNNs through the utilization of an attention mechanism. Under the guidance of thisprior knowledge, the proposed method enables the extraction of accurate and predictive features. Importantly, these extracted features are anticipated to retain the interpretability of the prior knowledge. The effectiveness of the proposed method is verified on the Tennessee Eastman chemical process dataset. The results show that proposed method achieved a fault diagnosis accuracy of 98.46%, which is significantly higher than similar existing methods. Furthermore, the robustness of the proposed method is analyzed by sensitivity analysis on hyperparameters, and the interpretability is revealed by visually analyzing its feature extraction process.

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“…Therefore, this process remains highly timeconsuming, with thousands of pictures to mask. Since image masking is an image segmentation problem, deep learning methods and Convolutional Neural Networks (CNNs) were also explored (Long et al, 2015;Liu et al, 2020). The main issue of these fully automatic methods is the amount of input data required for training models and the availability of proper image datasets for handling the image classification and semantic segmentation tasks.…”

Section: Related Workmentioning

confidence: 99%

Handling Critical Aspects in Massive Photogrammetric Digitization of Museum Assets

Farella

Morelli

Grilli

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In recent years, a growing interest in the 3D digitisation of museum assets has been pushed by the evident advantages of digital copies in supporting and advancing the knowledge, preservation and promotion of historical artefacts. Realising photo-realistic and precise digital twins of medium and small-sized movable objects implies several operations, still hiring open research problems and hampering the complete automation and derivation of satisfactory results while limiting processing time. The work examines some recurrent issues and potential solutions, summing up several experiences of photogrammetric-based massive digitisation projects. In particular, the article presents some insights into three crucial aspects of the photogrammetric pipeline. The first experiments tackle the Depth of Field (DoF) problem, especially when digitising small artefacts with macro-lenses. On the processing side, two decisive and time-consuming tasks are instead investigated: background masking and point cloud editing, exploring and proposing automatic solutions for speeding up the reconstruction process.

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Masked convolutional neural network for supervised learning problems

Cited by 8 publications

References 17 publications

A convolutional neural network model for EPID‐based non‐transit dosimetry

A convolutional neural network model for EPID‐based non‐transit dosimetry

Improving Accuracy and Interpretability of CNN-Based Fault Diagnosis through an Attention Mechanism

Handling Critical Aspects in Massive Photogrammetric Digitization of Museum Assets

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