scholarly journals Image-based patch selection for deep learning to improve automated Gleason grading in histopathological slides

2020 ◽  
Author(s):  
William Speier ◽  
Jiayun Li ◽  
Wenyuan Li ◽  
Karthik Sarma ◽  
Corey Arnold
Keyword(s):  
Deep Learning ◽  
Selection Process ◽  
Contextual Information ◽  
Image Features ◽  
Histopathological Analysis ◽  
Low Grade ◽  
Patch Selection ◽  
Gleason Grading ◽  
Segmentation Accuracy ◽  
Grid Based

AbstractAutomated Gleason grading can be a valuable tool for physicians when assessing risk and planning treatment for prostate cancer patients. Semantic segmentation provides pixel-wise Gleason predictions across an entire slide, which can be more informative than classification of pre-selected homogeneous regions. Deep learning methods can automatically learn visual semantics to accomplish this task, but training models on whole slides is impractical due to large image sizes and scarcity of fully annotated data. Patch-based methods can alleviate these problems, and have been shown to produce significant results in histopathology segmentation. However, the irregular contours of biopsies on slides makes performance highly dependent on patch selection. In the traditional grid-based strategy, many patches lie on biopsy boundaries, reducing segmentation accuracy due to a reduction in contextual information. In this paper, we propose an automatic patch selection process based on image features. This algorithm segments the biopsy and aligns patches based on the tissue contour to maximize the amount of contextual information in each patch. This method was used to generate patches for a fully convolutional network to segment high grade, low grade, and benign tissue from a set of 59 histopathological slides, and results were compared against manual physician labels. We show that using our image-based patch selection algorithm results in a significant improvement in segmentation accuracy over the traditional grid-based approach. Our results suggest that informed patch selection can be a valuable addition to an automated histopathological analysis pipeline.

Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT

2021 ◽  
Author(s):  
Aditi Iyer ◽  
Maria Thor ◽  
Ifeanyirochukwu Onochie ◽  
Jennifer Hesse ◽  
Kaveh Zakeri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Treatment Planning ◽  
Contextual Information ◽  
Ct Scans ◽  
Institutional Research ◽  
Similarity Coefficients ◽  
Inter Observer Variability ◽  
Segmentation Accuracy ◽  
Deep Learning Model ◽  
Segmentation Models

Abstract ObjectiveDelineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process.ApproachCT scans of 242 head and neck (H&N) cancer patients acquired from 2004-2009 at our institution were used to develop auto-segmentation models for the masseters, medial pterygoids, larynx, and pharyngeal constrictor muscle using DeepLabV3+. A cascaded architecture was used, wherein models were trained sequentially to spatially constrain each structure group based on prior segmentations. Additionally, an ensemble of models, combining contextual information from axial, coronal, and sagittal views was used to improve segmentation accuracy. Prospective evaluation was conducted by measuring the amount of manual editing required in 91 H&N CT scans acquired February-May 2021.Main resultsMedians and inter-quartile ranges of Dice Similarity Coefficients (DSC) computed on the retrospective testing set (N=24) were 0.87 (0.85-0.89) for the masseters, 0.80 (0.79- 0.81) for the medial pterygoids, 0.81 (0.79-0.84) for the larynx, and 0.69 (0.67-0.71) for the constrictor. Auto-segmentations, when compared to inter-observer variability in 10 randomly selected scans, showed better agreement (DSC) with each observer as compared to inter-observer DSC. Prospective analysis showed most manual modifications needed for clinical use were minor, suggesting auto-contouring could increase clinical efficiency. Trained segmentation models are available for research use upon request via https://github.com/cerr/CERR/wiki/Auto-Segmentation-models.SignificanceWe developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT and demonstrated its potential for use in treatment planning to limit complications post-RT. To the best of our knowledge, this is the only prospectively-validated deep learning-based model for segmenting chewing and swallowing structures in CT. Additionally, the segmentation models have been made open-source to facilitate reproducibility and multi-institutional research.

Colon cancer prediction on histological images using deep learning features and Bayesian optimized SVM

2021 ◽  
pp. 1-12
Author(s):  
Tina Babu ◽  
Tripty Singh ◽  
Deepa Gupta ◽  
Shahin Hameed
Keyword(s):  
Colon Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Detection System ◽  
Image Features ◽  
Support Vector ◽  
Histopathological Analysis ◽  
Automated Diagnosis ◽  
Public Datasets ◽  
High Level

Colon cancer is one of the highest cancer diagnosis mortality rates worldwide. However, relying on the expertise of pathologists is a demanding and time-consuming process for histopathological analysis. The automated diagnosis of colon cancer from biopsy examination played an important role for patients and prognosis. As conventional handcrafted feature extraction requires specialized experience to select realistic features, deep learning processes have been chosen as abstract high-level features may be extracted automatically. This paper presents the colon cancer detection system using transfer learning architectures to automatically extract high-level features from colon biopsy images for automated diagnosis of patients and prognosis. In this study, the image features are extracted from a pre-trained convolutional neural network (CNN) and used to train the Bayesian optimized Support Vector Machine classifier. Moreover, Alexnet, VGG-16, and Inception-V3 pre-trained neural networks were used to analyze the best network for colon cancer detection. Furthermore, the proposed framework is evaluated using four datasets: two are collected from Indian hospitals (with different magnifications 4X, 10X, 20X, and 40X) and the other two are public colon image datasets. Compared with the existing classifiers and methods using public datasets, the test results evaluated the Inception-V3 network with the accuracy range from 96.5% - 99% as best suited for the proposed framework.

scholarly journals Does Anatomical Contextual Information Improve 3D U-Net-Based Brain Tumor Segmentation?

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1159
Author(s):  
Iulian Emil Tampu ◽  
Neda Haj-Hosseini ◽  
Anders Eklund
Keyword(s):  
Brain Tumor ◽  
Contextual Information ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Mr Image ◽  
Brain Tumor Segmentation ◽  
Training Time ◽  
Probability Maps ◽  
Segmentation Accuracy ◽  
Model Training

Effective, robust, and automatic tools for brain tumor segmentation are needed for the extraction of information useful in treatment planning. Recently, convolutional neural networks have shown remarkable performance in the identification of tumor regions in magnetic resonance (MR) images. Context-aware artificial intelligence is an emerging concept for the development of deep learning applications for computer-aided medical image analysis. A large portion of the current research is devoted to the development of new network architectures to improve segmentation accuracy by using context-aware mechanisms. In this work, it is investigated whether or not the addition of contextual information from the brain anatomy in the form of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) masks and probability maps improves U-Net-based brain tumor segmentation. The BraTS2020 dataset was used to train and test two standard 3D U-Net (nnU-Net) models that, in addition to the conventional MR image modalities, used the anatomical contextual information as extra channels in the form of binary masks (CIM) or probability maps (CIP). For comparison, a baseline model (BLM) that only used the conventional MR image modalities was also trained. The impact of adding contextual information was investigated in terms of overall segmentation accuracy, model training time, domain generalization, and compensation for fewer MR modalities available for each subject. Median (mean) Dice scores of 90.2 (81.9), 90.2 (81.9), and 90.0 (82.1) were obtained on the official BraTS2020 validation dataset (125 subjects) for BLM, CIM, and CIP, respectively. Results show that there is no statistically significant difference when comparing Dice scores between the baseline model and the contextual information models (p > 0.05), even when comparing performances for high and low grade tumors independently. In a few low grade cases where improvement was seen, the number of false positives was reduced. Moreover, no improvements were found when considering model training time or domain generalization. Only in the case of compensation for fewer MR modalities available for each subject did the addition of anatomical contextual information significantly improve (p < 0.05) the segmentation of the whole tumor. In conclusion, there is no overall significant improvement in segmentation performance when using anatomical contextual information in the form of either binary WM, GM, and CSF masks or probability maps as extra channels.

scholarly journals A Lightweight Fusion Distillation Network for Image Deblurring and Deraining

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5312
Author(s):  
Yanni Zhang ◽  
Yiming Liu ◽  
Qiang Li ◽  
Jianzhong Wang ◽  
Miao Qi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Deblurring ◽  
Image Features ◽  
Image Feature ◽  
Model Complexity ◽  
Small Scale ◽  
Feature Maps ◽  
Learning Framework ◽  
Channel Information ◽  
Scale Spaces

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from a high computational burden. We propose a lightweight fusion distillation network (LFDN) for image deblurring and deraining to solve the above problems. The proposed LFDN is designed as an encoder–decoder architecture. In the encoding stage, the image feature is reduced to various small-scale spaces for multi-scale information extraction and fusion without much information loss. Then, a feature distillation normalization block is designed at the beginning of the decoding stage, which enables the network to distill and screen valuable channel information of feature maps continuously. Besides, an information fusion strategy between distillation modules and feature channels is also carried out by the attention mechanism. By fusing different information in the proposed approach, our network can achieve state-of-the-art image deblurring and deraining results with a smaller number of parameters and outperform the existing methods in model complexity.

Classification of papillary thyroid carcinoma histological images based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Yaning Liu ◽  
Lin Han ◽  
Hexiang Wang ◽  
Bo Yin
Keyword(s):  
Neural Network ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Image Features ◽  
Papillary Thyroid ◽  
Histological Image ◽  
Histological Images

Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images.

scholarly journals Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2866
Author(s):  
Fernando Navarro ◽  
Hendrik Dapper ◽  
Rebecca Asadpour ◽  
Carolin Knebel ◽  
Matthew B. Spraker ◽  
...  
Keyword(s):  
Deep Learning ◽  
Soft Tissue ◽  
Mr Imaging ◽  
Characteristic Curve ◽  
External Validation ◽  
Soft Tissue Sarcomas ◽  
Treatment Decision ◽  
Receiver Operator Characteristic Curve ◽  
Low Grade ◽  
Tumor Grading

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-18 ◽  
Author(s):  
Huimin Lu ◽  
Rui Yang ◽  
Zhenrong Deng ◽  
Yonglin Zhang ◽  
Guangwei Gao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Contextual Information ◽  
Image Features ◽  
Context Information ◽  
Global Information ◽  
Image Description ◽  
Image Captioning ◽  
Image Content ◽  
Single Feature ◽  
Proposed Model

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU , BLEU , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

scholarly journals Domain knowledge integration into deep learning for typhoon intensity classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maiki Higa ◽  
Shinya Tanahara ◽  
Yoshitaka Adachi ◽  
Natsumi Ishiki ◽  
Shin Nakama ◽  
...  
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Knowledge Integration ◽  
Cluster Formation ◽  
Satellite Image ◽  
Image Features ◽  
Feature Maps ◽  
Typhoon Intensity ◽  
Intensity Class ◽  
Intensity Classification

AbstractIn this report, we propose a deep learning technique for high-accuracy estimation of the intensity class of a typhoon from a single satellite image, by incorporating meteorological domain knowledge. By using the Visual Geometric Group’s model, VGG-16, with images preprocessed with fisheye distortion, which enhances a typhoon’s eye, eyewall, and cloud distribution, we achieved much higher classification accuracy than that of a previous study, even with sequential-split validation. Through comparison of t-distributed stochastic neighbor embedding (t-SNE) plots for the feature maps of VGG with the original satellite images, we also verified that the fisheye preprocessing facilitated cluster formation, suggesting that our model could successfully extract image features related to the typhoon intensity class. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to highlight the eye and the cloud distributions surrounding the eye, which are important regions for intensity classification; the results suggest that our model qualitatively gained a viewpoint similar to that of domain experts. A series of analyses revealed that the data-driven approach using only deep learning has limitations, and the integration of domain knowledge could bring new breakthroughs.

scholarly journals Structured (De)composable Representations Trained with Neural Networks

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 79
Author(s):  
Graham Spinks ◽  
Marie-Francine Moens
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Contextual Information ◽  
Learning To Learn ◽  
Class Label ◽  
Impact Performance ◽  
Novel Technique ◽  
Language Data ◽  
Concept Classes ◽  
Generic Representation

This paper proposes a novel technique for representing templates and instances of concept classes. A template representation refers to the generic representation that captures the characteristics of an entire class. The proposed technique uses end-to-end deep learning to learn structured and composable representations from input images and discrete labels. The obtained representations are based on distance estimates between the distributions given by the class label and those given by contextual information, which are modeled as environments. We prove that the representations have a clear structure allowing decomposing the representation into factors that represent classes and environments. We evaluate our novel technique on classification and retrieval tasks involving different modalities (visual and language data). In various experiments, we show how the representations can be compressed and how different hyperparameters impact performance.

Sign in / Sign up

Export Citation Format

Share Document