Can Transformers In-Context Learn Behavior of a Linear Dynamical System?

We investigate whether transformers can learn to track a random process when given observations of a related process and parameters of the dynamical system that relates them as context. More specifically, we consider a finite-dimensional state-space model described by the state transition matrix $F$, measurement matrices $h_1, \dots, h_N$, and the process and measurement noise covariance matrices $Q$ and $R$, respectively; these parameters, randomly sampled, are provided to the transformer along with the observations $y_1,\dots,y_N$ generated by the corresponding linear dynamical system. We argue that in such settings transformers learn to approximate the celebrated Kalman filter, and empirically verify this both for the task of estimating hidden states $\hat{x}_{N|1,2,3,...,N}$ as well as for one-step prediction of the $(N+1)^{st}$ observation, $\hat{y}_{N+1|1,2,3,...,N}$. A further study of the transformer's robustness reveals that its performance is retained even if the model's parameters are partially withheld. In particular, we demonstrate that the transformer remains accurate at the considered task even in the absence of state transition and noise covariance matrices, effectively emulating operations of the Dual-Kalman filter.

A Hybrid VDV Model for Automatic Diagnosis of Pneumothorax using Class-Imbalanced Chest X-rays Dataset

Pneumothorax, a life threatening disease, needs to be diagnosed immediately and efficiently. The prognosis in this case is not only time consuming but also prone to human errors. So an automatic way of accurate diagnosis using chest X-rays is the utmost requirement. To-date, most of the available medical images datasets have class-imbalance issue. The main theme of this study is to solve this problem along with proposing an automated way of detecting pneumothorax. We first compare the existing approaches to tackle the class-imbalance issue and find that data-level-ensemble (i.e. ensemble of subsets of dataset) outperforms other approaches. Thus, we propose a novel framework named as VDV model, which is a complex model-level-ensemble of data-level-ensembles and uses three convolutional neural networks (CNN) including VGG16, VGG-19 and DenseNet-121 as fixed feature extractors. In each data-level-ensemble features extracted from one of the pre-defined CNN are fed to support vector machine (SVM) classifier, and output from each data-level-ensemble is calculated using voting method. Once outputs from the three data-level-ensembles with three different CNN architectures are obtained, then, again, voting method is used to calculate the final prediction. Our proposed framework is tested on SIIM ACR Pneumothorax dataset and Random Sample of NIH Chest X-ray dataset (RS-NIH). For the first dataset, 85.17% Recall with 86.0% Area under the Receiver Operating Characteristic curve (AUC) is attained. For the second dataset, 90.9% Recall with 95.0% AUC is achieved with random split of data while 85.45% recall with 77.06% AUC is obtained with patient-wise split of data. For RS-NIH, the obtained results are higher as compared to previous results from literature However, for first dataset, direct comparison cannot be made, since this dataset has not been used earlier for Pneumothorax classification.

Automatic Diagnosis of Pneumothorax from Chest Radiographs: A Systematic Literature Review

Among various medical imaging tools, chest radiographs are the most important and widely used diagnostic tool for detection of thoracic pathologies. Researches are being carried out in order to propose robust automatic diagnostic tool for detection of pathologies from chest radiographs. Artificial Intelligence techniques especially deep learning methodologies have been found to be giving promising results in automating the field of medicine. Lot of research has been done for automatic and fast detection of pneumothorax from chest radiographs while proposing several frameworks based on artificial intelligence and machine learning techniques. This study summarizes the existing literature for the automatic detection of pneumothorax from chest x-rays along with describing the available chest radiographs datasets. The comparative analysis of the literature is also provided in terms of goodness and limitations of the existing literature along with highlighting the research gaps which need to be further explored. The paper provides a brief overview of the present work for pneumothorax detection for helping the researchers in selection of optimal approach for future research.