The dataset was split 80/20 for training and testing. Both models were trained with SGD (0.8 momentum, 0.00001 learning rate). In Stage I, one neuron and a sigmoid function were used to classify WBCs as normal or abnormal. The Stage II model classified WBCs into eight subtypes by using a dense layer of eight neurons and a SoftMax loss function. The model was built by utilizing an Intel® CoreTM i7-9750 h at 2.60 GHz 192 CPU with 16 GB of RAM and an NVIDIA GeForce RTX 2070 with a max-design. The algorithm was written in Python by using Keras and other image-processing libraries to extract handcrafted features.
Core i7 9750h
Manufactured by NVIDIA
Sourced in United States
The Core i7-9750H is a 6-core, 12-thread mobile processor from Intel. It features a base clock speed of 2.6 GHz and a max turbo frequency of 4.5 GHz. The processor is built on Intel's 14nm process technology and has a TDP of 45W.
Automatically generated - may contain errors
9 protocols using core i7 9750h
1
Automated WBC Classification System
2
Virtual Helicopter Deck-Landing Simulation
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Participants sat in front of a laptop (Intel CoreTM i7-9750H CPU @2.60 GHz with 8Go RAM, NVidia GeForce GTX 1050 GPU) operated by Windows 10 64-bit (Microsoft, Seattle, Washington, USA) and were immersed on the screen (35.4 × 19.9 cm, 1920 × 1080 px) on board a virtual NATO Frigate Helicopter. Pre-programmed rate of descent of the virtual helicopter could be changed by pressing keyboard keys to attempt or abort deck-landing maneuvers onto the deck of a Lafayette-type frigate. Both the flight dynamics of the virtual helicopter and the ship were computed online based on Eq. 1 . The visual scene was rendered on the laptop screen with a Unity3D engine (2020.3.2.f1, Unity Technologies ApS, Unity 3D.com).
3
Markerless Animal Pose Tracking with DeepLabCut
4
Immersive Virtual Reality Experiment
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The experiment was performed in a virtual reality environment realized with the HTC Vive Pro Eye HMD to provide a highly immersive experience. It is equipped with two AMOLED screens, with a resolution of 1440 × 1600 pixels per eye, a refresh rate of 90 Hz, and a field of view of 110°. Unity was integrated into the HMD via the Steam VR asset to control the experimental procedure and collect data. The experiment ran on a laptop with Windows 10 Home (64-bit), Intel Core i7 -9750H, 32 GB RAM, and the NVIDIA GeForce RTX 2070 graphics card.
5
Markerless Animal Body Tracking
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Markerless tracking of animal body parts was conducted using version 2.2.1.1 of the DeepLabCut (DLC) Toolbox (40 (link),44 (link)) and analysis of movement features based on these tracked coordinates was conducted in Matlab R2020b (Mathworks). All DLC analysis was conducted on a Dell G7–7590 laptop running Windows 10 with an Intel Core i7-9750H CPU, 2.60Ghz, 16 GB RAM, and an NVIDIA GeForce RTX 2080 Max-Q 8GB GPU. DeepLabCut 2.2.1.1 was installed in an Anaconda environment with Python 3.8.4, CUDA 11.7 and Tensorflow 2.10.
6
Incremental Learning with Distillation
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The proposed framework is implemented using TensorFlow 1.14 and Keras 2.0.0 with Python 3.7.4 on the Anaconda platform. Some of the utility functions are also implemented using MATLAB R2020a. The proposed framework's training was conducted in two phases where the candidate classification model minimized the L IL loss function in each iteration. The number of epochs (in each training increment) was 20 (and the number of cycles in each epoch varies as per each dataset). Also, during each training increment, we fed the candidate network with around 20% of the original training data (where 10% were used for the distillation process and the remaining 10% were used to learn the newly added classes). Apart from this, we used ADADELTA [45] as an optimizer, and the training was conducted on the machine with a Core i7-9750H@2.6 GHz processor, 32GB DDR4 RAM, and NVIDIA RTX 2080 Max-Q GPU with cuDNN v7.5 and a CUDA Toolkit 10.1.243.
7
Deep Learning for Image Classification
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The stated methodology is coded in MATLABmathsizesmall® using its Deep Learning Toolbox™ on a laptop running the Windows® 10 operating system, with an Intel Core i7-9750H processor, 16 GB of RAM, and an Nvidia GeForce RTX™2060 graphics card that has 6 GB of dedicated VRAM.
8
Machine Learning on Laptop Hardware
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All experiments in this study were performed on a laptop with Intel® Core™ i7-9750H CPU, 16GB DDR4 RAM, and NVIDIA GeForce GTX 1660 Ti, GDDR6 6GB GPU. Application codes were written in Python using Keras [27 ] from DL libraries and Scikit-learn [28 ] from ML libraries.
9
Stability of Radiomics Features in Lung CT
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The experiments were carried out on a laptop PC with Intel Core i7-9750H CPU @ 2.60 GHz, 32 GB RAM, NVIDIA Quadro T1000 (4 GB) graphics card and Windows 10 Pro 64-bit operating system. The implementation was based on Python 3.8.6, with functions from dicom-parser 0.1.6 [47 ], NumPy 1.18.5 [48 (link)], Pandas 1.1.3 [49 ,50 (link)], pylidc 0.2.2 [51 (link),52 ], pynrrd 0.4.2 [53 ] and Py-Radiomics 3.0.1 [42 ,54 (link)]. For reproducible research purposes, all the code and settings are available on the following GitHub repository: https://github.com/bianconif/stability_radiomics_features_lung_ct , accessed on 3 July 2021.
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