Generalization of Artificial Intelligence Models in Medical Imaging: A Case-Based Review

The discussions around Artificial Intelligence (AI) and medical imaging are centered around the success of deep learning algorithms. As new algorithms enter the market, it is important for practicing radiologists to understand the pitfalls of various AI algorithms. This entails having a basic understanding of how algorithms are developed, the kind of data they are trained on, and the settings in which they will be deployed. As with all new technologies, use of AI should be preceded by a fundamental understanding of the risks and benefits to those it is intended to help. This case-based review is intended to point out specific factors practicing radiologists who intend to use AI should consider

Tekoäly päätöksenteossa: Algoritmit, data ja yhdenvertaisuus

Tekoälyn käyttö päätöksenteossa on saanut viime vuosina suurta huomiota ja se on käytössä jo esimerkiksi rahoituksessa, kunnan palveluissa ja terveydenhuollossa. Tekoäly tarjoaa monia etuja työkaluna eri aloilla, mutta siihen liittyy myös mahdollisia ongelmia, kun käyttökohteena on päätöksenteko. Tutkielmassa keskitytään tuomaan esiin tekoälyn perustaa algoritmeista ja datasta, sekä joitain sen käyttökohteita. Tutkielma pyrkii tarjoamaan vastauksen siihen, miten tekoälyä hyödynnetään ja mitä mahdollisia ongelmia siitä voi nousta. Tutkielma on toteutettu kirjallisuuskatsauksena aiemmin julkaistuun aineistoon perustuen. Tutkielman on tarkoitus olla informatiivinen katsaus tekoälyn käyttöön päätöksenteossa, ja sisällön yhtenäisyyden vuoksi siitä on jätetty osa koneoppimisen malleista pois, kuten valvomattoman koneoppimisen vahvistusoppimismalli. Tavoitteena on tutkia tekoälyn käyttökohteita sekä sitä, millaiselle algoritmiselle perustalle se voi rakentua. Tutkimuksessa tarkastellaan erilaisia tekoälypohjaisia päätöksentekojärjestelmiä eri aloilla ja arvioidaan niiden vaikutusta päätöksentekoprosesseihin. Lisäksi tutkimuksessa analysoidaan tekoälyn eettisiä ja sosiaalisia vaikutuksia päätöksenteossa ja ehdotetaan ohjeita tekoälyn vastuulliseen käyttöön. Tutkielma pyrkii vastaamaan kysymykseen ”Miten tekoälyä hyödynnetään päätöksenteossa ja mitä mahdollisia ongelmia siitä voi nousta?”. Tutkielma vastaa tutkimuskysymykseen tietoaineistoon perustuen, mahdollisten esiin nousevien ongelmien ollessa painottuneena vinoumiin ja sen vaikutukseen yhdenvertaisuudessa. Tuloksissa osoitetaan tekoälyn sovelluskohteita eri aloilla ja sen mahdollisia hyötyjä sekä haittoja täsmälääketieteen alalla. Tutkimustulos osoittaa myös sen, millaisia vaikutuksia datan ja algoritmien vinoumilla on tekoälyalgoritmien tekemiin päätöksiin sekä sen, millaisia koneoppimismalleja on mahdollista hyödyntää tekoälyalgoritmien kehityksessä ohjatun ja ohjaamattoman oppimisen kautta

Image synthesis of monoenergetic CT image in dual-energy CT using kilovoltage CT with deep convolutional generative adversarial networks

Purpose: To synthesize a dual-energy computed tomography (DECT) image from an equivalent kilovoltage computed tomography (kV-CT) image using a deep convolutional adversarial network. Methods: A total of 18,084 images of 28 patients are categorized into training and test datasets. Monoenergetic CT images at 40, 70, and 140 keV and equivalent kVCT images at 120 kVp are reconstructed via DECT and are defined as the reference images. An image prediction framework is created to generate monoenergetic computed tomography (CT) images from kV-CT images. The accuracy of the images generated by the CNN model is determined by evaluating the mean absolute error (MAE), mean square error (MSE), relative root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information between the synthesized and reference monochromatic CT images. Moreover, the pixel values between the synthetic and reference images are measured and compared using a manually drawn region of interest (ROI). Results: The difference in the monoenergetic CT numbers of the ROIs between the synthetic and reference monoenergetic CT images is within the standard deviation values. The MAE, MSE, RMSE, and SSIM are the smallest for the image conversion of 120 kVp to 140 keV. The PSNR is the smallest and the MI is the largest for the synthetic 70 keV image. Conclusions: The proposed model can act as a suitable alternative to the existing methods for the reconstruction of monoenergetic CT images in DECT from single-energy CT images

Enabling Intelligent IoTs for Histopathology Image Analysis Using Convolutional Neural Networks

Medical imaging is an essential data source that has been leveraged worldwide in healthcare systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption. We analyze classification accuracy, energy estimation, and hardware utilization metrics to evaluate our method. First, the original RGBcolored images are utilized for the training phase, and then compression methods such as channel reduction and sparsity are applied. Our results show an accuracy increase of 6% from RGB on 32-bit (baseline) to the optimized representation of sparsity on RGB with a lower bit-width, i.e., \u3c8:8\u3e. For energy estimation on the used CNN model, we found that the energy used in RGB color mode with 32-bit is considerably higher than the other lower bit-width and compressed color modes. Moreover, we show that lower bit-width implementations yield higher resource utilization and a lower memory bottleneck ratio. This work is suitable for inference on energy-limited devices, which are increasingly being used in the Internet of Things (IoT) systems that facilitate healthcare systems

An Investigation of a Convolution Neural Network Architecture for Detecting Distracted Pedestrians

The risk of pedestrian accidents has increased due to the distracted walking increase. The research in the autonomous vehicles industry aims to minimize this risk by enhancing the route planning to produce safer routes. Detecting distracted pedestrians plays a significant role in identifying safer routes and hence decreases pedestrian accident risk. Thus, this research aims to investigate how to use the convolutional neural networks for building an algorithm that significantly improves the accuracy of detecting distracted pedestrians based on gathered cues. Particularly, this research involves the analysis of pedestrian’ images to identify distracted pedestrians who are not paying attention when crossing the road. This work tested three different architectures of convolutional neural networks. These architectures are Basic, Deep, and AlexNet. The performance of the three architectures was evaluated based on two datasets. The first is a new training dataset called SCIT and created by this work based on recorded videos of volunteers from Sheridan College Institute of Technology. The second is a public dataset called PETA, which was made up of images with various resolutions. The ConvNet model with the Deep architecture outperformed the Basic and AlexNet architectures in detecting distracted pedestrian