scholarly journals Computationally efficient target classification in multispectral image data with Deep Neural Networks

2016 ◽  
Author(s):  
Lukas Cavigelli ◽  
Dominic Bernath ◽  
Michele Magno ◽  
Luca Benini
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Image Data ◽  
Multispectral Image ◽  
Target Classification ◽  
Computationally Efficient

scholarly journals Towards pixel-to-pixel deep nucleus detection in microscopy images

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fuyong Xing ◽  
Yuanpu Xie ◽  
Xiaoshuang Shi ◽  
Pingjun Chen ◽  
Zizhao Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Image Data ◽  
Fine Tuning ◽  
Cell Detection ◽  
Imaging Protocol ◽  
Microscopy Image ◽  
Microscopy Images ◽  
Target Data

Abstract Background Nucleus or cell detection is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. Results We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. Conclusions We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

scholarly journals Automated and Manual Quantification of Tumour Cellularity in Digital Slides for Tumour Burden Assessment

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Shazia Akbar ◽  
Mohammad Peikari ◽  
Sherine Salama ◽  
Azadeh Yazdan Panah ◽  
Sharon Nofech-Mozes ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Traditional Approach ◽  
Quantitative Measure ◽  
Image Data ◽  
Qualitative Assessment ◽  
Residual Cancer Burden ◽  
Automated Algorithms ◽  
Tumour Cellularity

Abstract The residual cancer burden index is an important quantitative measure used for assessing treatment response following neoadjuvant therapy for breast cancer. It has shown to be predictive of overall survival and is composed of two key metrics: qualitative assessment of lymph nodes and the percentage of invasive or in situ tumour cellularity (TC) in the tumour bed (TB). Currently, TC is assessed through eye-balling of routine histopathology slides estimating the proportion of tumour cells within the TB. With the advances in production of digitized slides and increasing availability of slide scanners in pathology laboratories, there is potential to measure TC using automated algorithms with greater precision and accuracy. We describe two methods for automated TC scoring: 1) a traditional approach to image analysis development whereby we mimic the pathologists’ workflow, and 2) a recent development in artificial intelligence in which features are learned automatically in deep neural networks using image data alone. We show strong agreements between automated and manual analysis of digital slides. Agreements between our trained deep neural networks and experts in this study (0.82) approach the inter-rater agreements between pathologists (0.89). We also reveal properties that are captured when we apply deep neural network to whole slide images, and discuss the potential of using such visualisations to improve upon TC assessment in the future.

Enhanced Fusion of Deep Neural Networks for Classification of Benchmark High-Resolution Image Data Sets

2018 ◽  
Vol 15 (9) ◽  
pp. 1451-1455 ◽  
Author(s):  
Grant J. Scott ◽  
Kyle C. Hagan ◽  
Richard A. Marcum ◽  
James Alex Hurt ◽  
Derek T. Anderson ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Deep Neural Networks ◽  
Image Data ◽  
Data Sets ◽  
Resolution Image ◽  
High Resolution Image

scholarly journals How Many Features is an Image Worth? Multi-Channel CNN for Steering Angle Prediction in Autonomous Vehicles

2021 ◽  
Author(s):  
Jason Munger ◽  
Carlos W. Morato
Keyword(s):  
Neural Networks ◽  
Autonomous Vehicles ◽  
Deep Neural Networks ◽  
Spatial Information ◽  
Image Data ◽  
Input Image ◽  
Steering Angle ◽  
Rgb Images ◽  
And Performance ◽  
The Individual

This project explores how raw image data obtained from AV cameras can provide a model with more spatial information than can be learned from simple RGB images alone. This paper leverages the advances of deep neural networks to demonstrate steering angle predictions of autonomous vehicles through an end-to-end multi-channel CNN model using only the image data provided from an onboard camera. Image data is processed through existing neural networks to provide pixel segmentation and depth estimates and input to a new neural network along with the raw input image to provide enhanced feature signals from the environment. Various input combinations of Multi-Channel CNNs are evaluated, and their effectiveness is compared to single CNN networks using the individual data inputs. The model with the most accurate steering predictions is identified and performance compared to previous neural networks.

Outperforming Dermatologist-Level Skin Cancer Classification via Enhanced Training of Deep Neural Networks (Preprint)

2018 ◽  
Author(s):  
Titus Josef Brinker ◽  
Achim Hekler ◽  
Christof von Kalle
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Deep Neural Networks ◽  
Challenge Test ◽  
Image Data ◽  
Cancer Classification ◽  
Receiver Operating Curve ◽  
Training Procedure ◽  
Average Precision ◽  
Test Sets

BACKGROUND In recent months, multiple publications have demonstrated the use of convolutional neural networks (CNN) to classify images of skin cancer as precisely as dermatologists. These CNNs failed to outperform the International Symposium on Biomedical Imaging (ISBI) 2016 challenge in terms of average precision, however, so the technical progress represented by these studies is limited. In addition, the available reports are difficult to reproduce, due to incomplete descriptions of training procedures and the use of proprietary image databases. These factors prevent the comparison of various CNN classifiers in equal terms. OBJECTIVE To demonstrate the training of an image-classifier CNN that outperforms the winner of the ISBI 2016 challenge by using open source images exclusively. METHODS A detailed description of the training procedure is reported while the used images and test sets are disclosed fully, to insure the reproducibility of our work. RESULTS Our CNN classifier outperforms all recent attempts to classify the original ISBI 2016 challenge test data (full set of 379 test images), with an average precision of 0.709 (vs. 0.637 of the ISBI winner) and with an area under the receiver operating curve of 0.85. CONCLUSIONS This work illustrates the potential for improving skin cancer classification with enhanced training procedures for CNNs, while avoiding the use of costly equipment or proprietary image data.

scholarly journals DIGDH: A Novel Framework of Difference Image Grafting Deep Hiding for Image Data Hiding

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 151
Author(s):  
Xintao Duan ◽  
Lei Li ◽  
Yao Su ◽  
Wenxin Wang ◽  
En Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Data Hiding ◽  
Deep Neural Networks ◽  
Random Distribution ◽  
Image Data ◽  
Experimental Results ◽  
The Real ◽  
Difference Image ◽  
Audio Media

Data hiding is the technique of embedding data into video or audio media. With the development of deep neural networks (DNN), the quality of images generated by novel data hiding methods based on DNN is getting better. However, there is still room for the similarity between the original images and the images generated by the DNN models which were trained based on the existing hiding frameworks to improve, and it is hard for the receiver to distinguish whether the container image is from the real sender. We propose a framework by introducing a key_img for using the over-fitting characteristic of DNN and combined with difference image grafting symmetrically, named difference image grafting deep hiding (DIGDH). The key_img can be used to identify whether the container image is from the real sender easily. The experimental results show that without changing the structures of networks, the models trained based on the proposed framework can generate images with higher similarity to original cover and secret images. According to the analysis results of the steganalysis tool named StegExpose, the container images generated by the hiding model trained based on the proposed framework is closer to the random distribution.

scholarly journals Microfluidic Droplet Detection via Region-Based and Single-Pass Convolutional Neural Networks with Comparison to Conventional Image Analysis Methodologies

2021 ◽  
Author(s):  
Gregory Rutkowski ◽  
Ilgar Azizov ◽  
Evan Unmann ◽  
Marcin Dudek ◽  
Brian Arthur Grimes
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Data Gathering ◽  
Image Data ◽  
Validation Dataset ◽  
Detection Accuracy ◽  
Microfluidic Systems ◽  
Training Set ◽  
Analysis Pipeline

As the complexity of microfluidic experiments and the associated image data volumes scale, traditional feature extraction approaches begin to struggle at both detection and analysis pipeline throughput. Deep-neural networks trained to detect certain objects are rapidly emerging as data gathering tools that can either match or outperform the analysis capabilities of the conventional methods used in microfluidic emulsion science. We demonstrate that various convolutional neural networks can be trained and used as droplet detectors in a wide variety of microfluidic systems. A generalized microfluidic droplet training and validation dataset was developed and used to tune two versions of the You Only Look Once (YOLOv3/YOLOv5) model as well as Faster R-CNN. Each model was used to detect droplets in mono- and polydisperse flow cell systems. The detection accuracy of each model shows excellent statistical symmetry with an implementation of the Hough transform as well as relevant ImageJ plugins. The models were successfully used as droplet detectors in non-microfluidic micrograph observations, where these data were not included in the training set. The models outperformed the traditional methods in more complex, porous-media simulating chip architectures with a significant speedup to per-frame analysis times. Implementing these neural networks as the primary detectors in these microfluidic systems not only makes the data pipelining more efficient, but opens the door for live detection and development of autonomous microfluidic experimental platforms. <br>

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