This study investigates the integration of diverse patient data sources into multimodal language models for automated chest X-ray (CXR) report generation.Traditionally, CXR report generation relies solely on data from a patient's CXR exam, overlooking valuable information from patient electronic health records.Utilising the MIMIC-CXR and MIMIC-IV-ED datasets, we investigate the use of patient data from emergency department (ED) records -such as vital signs measured and medicines reconciled during an ED stay -for CXR report generation, with the aim of enhancing diagnostic accuracy.We also investigate conditioning CXR report generation on the clinical history section of radiology reports, which has been overlooked in the literature.We introduce a novel approach to transform these heterogeneous data sources into patient data embeddings that prompt a multimodal language model (CXRMate-ED).Our comprehensive evaluation indicates that using a broader set of patient data significantly enhances diagnostic accuracy.The model, training code, and dataset are publicly available.1,2 INDICATION: Evaluate for pneumonia.HISTORY: Asthma and wheezing for two days.COMPARISONS: Chest radiograph ___. FINDINGS:The lungs are clear.There is no pleural effusion or pneumothorax.There is no focal airspace consolidation to suggest pneumonia.Accounting for technique, the heart size is normal.The mediastinal contours are unremarkable.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

Deep learning models are powerful tools for addressing challenging medical imaging problems. However, for an ever-growing range of applications, interpreting a model’s prediction remains non-trivial. Understanding decisions made by black-box algorithms is critical, and assessing their fairness and susceptibility to bias is a key step towards healthcare deployment. In this paper, we propose DBCE (Deformation Based Counterfactual Explainability). We optimise a diffeomorphic transformation that deforms a given input image to change the prediction of the model. This provides anatomically meaningful saliency maps indicating tissue atrophy and expansion, which can be easily interpreted by clinicians. In our test case, DBCE replicates the transition of a patient from healthy control (HC) to Alzheimer’s disease (AD). We benchmark DBCE against three commonly used saliency methods. We show that it provides more meaningful saliency maps when applied to one subject and disease-consistent atrophy patterns when used over a larger cohort. In addition, our method fulfils a recent sanity check and is repeatable for different model initialisations in contrast to classical sensitivity-based methods.

Automatically generating a report from a patient’s Chest X-rays (CXRs) is a promising solution to reducing clinical workload and improving patient care. However, current CXR report generators—which are predominantly encoder-to-decoder models—lack the diagnostic accuracy to be deployed in a clinical setting. To improve CXR report generation, we investigate warm starting the encoder and decoder with recent open-source computer vision and natural language processing checkpoints, such as the Vision Transformer (ViT) and PubMedBERT. To this end, each checkpoint is evaluated on the MIMIC-CXR and IU X-ray datasets. Our experimental investigation demonstrates that the Convolutional vision Transformer (CvT) ImageNet-21K and the Distilled Generative Pre-trained Transformer 2 (DistilGPT2) checkpoints are best for warm starting the encoder and decoder, respectively. Compared to the state-of-the-art (M2 Transformer Progressive), CvT2DistilGPT2 attained an improvement of 8.3% for CE F-1, 1.8% for BLEU-4, 1.6% for ROUGE-L, and 1.0% for METEOR. The reports generated by CvT2DistilGPT2 have a higher similarity to radiologist reports than previous approaches. This indicates that leveraging warm starting improves CXR report generation. Code and checkpoints for CvT2DistilGPT2 are available at https://github.com/aehrc/cvt2distilgpt2.

Speech enhancement plays an essential role in a wide range of speech processing applications. Recent studies on speech enhancement tend to investigate how to effectively capture the long-term contextual dependencies of speech signals to boost performance. However, these studies generally neglect the time-frequency (T-F) distribution information of speech spectral components, which is equally important for speech enhancement. In this paper, we propose a simple yet very effective network module, which we term the T-F attention (TFA) module, that uses two parallel attention branches, i.e., time-frame attention and frequency-channel attention, to explicitly exploit position information to generate a 2-D attention map to characterise the salient T-F speech distribution. We validate our TFA module as part of two widely used backbone networks (residual temporal convolution network and Transformer) and conduct speech enhancement with four most popular training objectives. Our extensive experiments demonstrate that our proposed TFA module consistently leads to substantial enhancement performance improvements in terms of the five most widely used objective metrics, with negligible parameter overheads. In addition, we further evaluate the efficacy of speech enhancement as a front-end for a downstream speech recognition task. Our evaluation results show that the TFA module significantly improves the robustness of the system to noisy conditions.

Most studies on speech enhancement generally don’t explicitly consider the energy distribution of speech in time-frequency (T-F) representation, which is important for accurate prediction of mask or spectra. In this paper, we present a simple yet effective T-F attention (TFA) module, where a 2-D attention map is produced to provide differentiated weights to the spectral components of T-F representation. To validate the effectiveness of our proposed TFA module, we use the residual temporal convolution network (ResTCN) as the backbone network and conduct extensive experiments on two commonly used training targets. Our experiments demonstrate that applying our TFA module significantly improves the performance in terms of five objective evaluation metrics with negligible parameter overhead. The evaluation results show that the proposed ResTCN with the TFA module (ResTCN+TFA) consistently outperforms other baselines by a large margin.

Despite much progress, most temporal convolutional networks (TCN) based speech enhancement models are mainly focused on modeling the long-term temporal contextual dependencies of speech frames, without taking into account the distribution information of speech signal in frequency dimension. In this study, we propose a frequency dimension adaptive attention (FAA) mechanism to improve TCNs, which guides the model selectively emphasize the frequency-wise features with important speech information and also improves the representation capability of network. Our extensive experimental investigation demonstrates that the proposed FAA mechanism is able to consistently provide significant improvements in terms of speech quality (PESQ), intelligibility (STOI) and three other composite metrics. More promisingly, it has better generalization ability to real-world noisy environment.

Convolutional neural networks (CNNs) with residual links (ResNets) and causal dilated convolutional units have been the network of choice for deep learning approaches to speech enhancement. While residual links improve gradient flow during training, feature diminution of shallow layer outputs can occur due to repetitive summations with deeper layer outputs. One strategy to improve feature re-usage is to fuse both ResNets and densely connected CNNs (DenseNets). DenseNets, however, over-allocate parameters for feature re-usage. Motivated by this, we propose the residual-dense lattice network (RDL-Net), which is a new CNN for speech enhancement that employs both residual and dense aggregations without over-allocating parameters for feature re-usage. This is managed through the topology of the RDL blocks, which limit the number of outputs used for dense aggregations. Our extensive experimental investigation shows that RDL-Nets are able to achieve a higher speech enhancement performance than CNNs that employ residual and/or dense aggregations. RDL-Nets also use substantially fewer parameters and have a lower computational requirement. Furthermore, we demonstrate that RDL-Nets outperform many state-of-the-art deep learning approaches to speech enhancement. Availability: https://github.com/nick-nikzad/RDL-SE.

We introduce sum-product networks (SPNs) for robust speech processing through a simple robust automatic speaker identification (ASI) task. SPNs are deep probabilistic graphical models capable of answering multiple probabilistic queries. We show that SPNs are able to remain robust by using the marginal probability density function (PDF) of the spectral features that reliably represent speech. Though current SPN toolkits and learning algorithms are in their infancy, we aim to show that SPNs have the potential to become a useful tool for robust speech processing in the future. SPN speaker models are evaluated here on real-world non-stationary and coloured noise sources at multiple signal-to-noise ratio (SNR) levels. In terms of ASI accuracy, we find that SPN speaker models are more robust than two recent convolutional neural network (CNN)-based ASI systems. Additionally, SPN speaker models consist of significantly fewer parameters than their CNN-based counterparts. The results indicate that SPN speaker models could be a robust, parameter-efficient alternative for ASI. Additionally, this work demonstrates that SPNs have potential in related tasks, such as robust automatic speech recognition (ASR) and automatic speaker verification (ASV). Availability: The SPN ASI system is available at https://github.com/anicolson/SPN-ASI.

The existing Kalman filter (KF) suffers from poor estimates of the noise variance and the linear prediction coefficients (LPCs) in real-world noise conditions. This results in a degraded speech enhancement performance. In this paper, a deep learning approach is used to more accurately estimate the noise variance and LPCs, enabling the KF to enhance speech in various noise conditions. Specifically, a deep learning approach to MMSE-based noise power spectral density (PSD) estimation, called DeepMMSE, is used. The estimated noise PSD is used to compute the noise variance. We also construct a whitening filter with its coefficients computed from the estimated noise PSD. It is then applied to the noisy speech, yielding pre-whitened speech for computing the LPCs. The improved noise variance and LPC estimates enable the KF to minimise the residual noise and distortion in the enhanced speech. Experimental results show that the proposed method exhibits higher quality and intelligibility in the enhanced speech than the benchmark methods in various noise conditions for a wide-range of SNR levels.

The existing augmented Kalman filter (AKF) suffers from poor LPC estimates in real-world noise conditions, which degrades the speech enhancement performance. In this paper, a deep learning technique exploits the LPC estimates for the AKF to enhance speech in various noise conditions. Specifically, a deep residual network is used to estimate the noise PSD for computing noise LPCs. A whitening filter is also implemented with the noise LPCs to pre-whiten the noisy speech signal prior to estimating the speech LPCs. It is shown that the improved speech and noise LPCs enable the AKF to minimize the residual noise as well as distortion in the enhanced speech. Experimental results show that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than the benchmark methods in various noise conditions for a wide-range of SNR levels.

An accurate noise power spectral density (PSD) tracker is an indispensable component of a single-channel speech enhancement system. Bayesian-motivated minimum mean-square error (MMSE)-based noise PSD estimators have been the most prominent in recent time. However, they lack the ability to track highly non-stationary noise sources due to current methods of a priori signal-to-noise (SNR) estimation. This is caused by the underlying assumption that the noise signal changes at a slower rate than the speech signal. As a result, MMSE-based noise PSD trackers exhibit a large tracking delay and produce noise PSD estimates that require bias compensation. Motivated by this, we propose an MMSE-based noise PSD tracker that employs a temporal convolutional network (TCN) a priori SNR estimator. The proposed noise PSD tracker, called DeepMMSE makes no assumptions about the characteristics of the noise or the speech, exhibits no tracking delay, and produces an accurate estimate that requires no bias correction. Our extensive experimental investigation shows that the proposed DeepMMSE method outperforms state-of-the-art noise PSD trackers and demonstrates the ability to track abrupt changes in the noise level. Furthermore, when employed in a speech enhancement framework, the proposed DeepMMSE method is able to outperform state-of-the-art noise PSD trackers, as well as multiple deep learning approaches to speech enhancement. Availability: DeepMMSE is available at: https://github.com/anicolson/DeepXi.

Recently, the focus of speech enhancement research has shifted from minimum mean-square error (MMSE) approaches, like the MMSE short-time spectral amplitude (MMSE-STSA) estimator, to state-of-the-art masking- and mapping-based deep learning approaches. We aim to bridge the gap between these two differing speech enhancement approaches. Deep learning methods for MMSE approaches are investigated in this work, with the objective of producing intelligible enhanced speech at a high quality. Since the speech enhancement performance of an MMSE approach improves with the accuracy of the used a priori signal-to-noise ratio (SNR) estimator, a residual long short-term memory (ResLSTM) network is utilised here to accurately estimate the a priori SNR. MMSE approaches utilising the ResLSTM a priori SNR estimator are evaluated using subjective and objective measures of speech quality and intelligibility. The tested conditions include real-world non-stationary and coloured noise sources at multiple SNR levels. MMSE approaches utilising the proposed a priori SNR estimator are able to achieve higher enhanced speech quality and intelligibility scores than recent masking- and mapping-based deep learning approaches. The results presented in this work show that the performance of an MMSE approach to speech enhancement significantly increases when utilising deep learning. Availability: The proposed a priori SNR estimator is available at: https://github.com/anicolson/DeepXi.

An accurate Ideal Binary Mask (IBM) estimate is essential for Missing Feature Theory (MFT)-based speaker identifica-tion, as incorrectly labelled spectral components (where a com-ponent is either reliable or unreliable) will degrade the perfor-mance of an Automatic Speaker Identification (ASI) system ad-versely in the presence of noise. In this work a Bidirectional Re-current Neural Network (BRNN) with Long-Short Term Mem-ory (LSTM) cells is proposed for improved IBM estimation. The proposed system had an average IBM estimate accuracy improvement of 4.5% and an average MFT-based speaker iden-tification accuracy improvement of 3.1% over all tested SNRdB levels, when compared to the previously proposed Multilayer Perceptron (MLP)-IBM estimator. When used for speech en-hancement the proposed system had an average MOS-LQO (ob-jective quality measure) improvement of 0.32 and an average QSTI (objective intelligibility measure) improvement of 0.01 over all tested SNRdB levels, when compared to the MLP-IBM estimator. The results presented in this work highlight the effec-tiveness of the proposed BRNN-IBM estimator for MFT-based speaker identification and IBM-based speech enhancement.