Knowledge Models for Cancer Clinical Practice Guidelines : Construction, Management and Usage in Question Answering

An automated knowledge modeling algorithm for Cancer Clinical Practice Guidelines (CPGs) extracts the knowledge contained in the CPG documents and transforms it into a programmatically interactable, easy-to-update structured model with minimal human intervention. The existing automated algorithms have minimal scope and cannot handle the varying complexity of the knowledge content in the CPGs for different cancer types. This work proposes an improved automated knowledge modeling algorithm to create knowledge models from the National Comprehensive Cancer Network (NCCN) CPGs in Oncology for different cancer types. The proposed algorithm has been evaluated with NCCN CPGs for four different cancer types. We also proposed an algorithm to compare the knowledge models for different versions of a guideline to discover the specific changes introduced in the treatment protocol of a new version. We created a question-answering (Q&A) framework with the guideline knowledge models as the augmented knowledge base to study our ability to query the knowledge models. We compiled a set of 32 question-answer pairs derived from two reliable data sources for the treatment of Non-Small Cell Lung Cancer (NSCLC) to evaluate the Q&A framework. The framework was evaluated against the question-answer pairs from one data source, and it can generate the answers with 54.5% accuracy from the treatment algorithm and 81.8% accuracy from the discussion part of the NCCN NSCLC guideline knowledge model.

A Hybrid-Layered System for Image-Guided Navigation and Robot Assisted Spine Surgery

In response to the growing demand for precise and affordable solutions for Image-Guided Spine Surgery (IGSS), this paper presents a comprehensive development of a Robot-Assisted and Navigation-Guided IGSS System. The endeavor involves integrating cutting-edge technologies to attain the required surgical precision and limit user radiation exposure, thereby addressing the limitations of manual surgical methods. We propose an IGSS workflow and system architecture employing a hybrid-layered approach, combining modular and integrated system architectures in distinctive layers to develop an affordable system for seamless integration, scalability, and reconfigurability. We developed and integrated the system and extensively tested it on phantoms and cadavers. The proposed system's accuracy using navigation guidance is 1.020 mm, and robot assistance is 1.11 mm on phantoms. Observing a similar performance in cadaveric validation where 84% of screw placements were grade A, 10% were grade B using navigation guidance, 90% were grade A, and 10% were grade B using robot assistance as per the Gertzbein-Robbins scale, proving its efficacy for an IGSS. The evaluated performance is adequate for an IGSS and at par with the existing systems in literature and those commercially available. The user radiation is lower than in the literature, given that the system requires only an average of 3 C-Arm images per pedicle screw placement and verification

Online Action Representation using Change Detection and Symbolic Programming

This paper addresses the critical need for online action representation, which is essential for various applications like rehabilitation, surveillance, etc. The task can be defined as representation of actions as soon as they happen in a streaming video without access to video frames in the future. Most of the existing methods use predefined window sizes for video segments, which is a restrictive assumption on the dynamics. The proposed method employs a change detection algorithm to automatically segment action sequences, which form meaningful sub-actions and subsequently fit symbolic generative motion programs to the clipped segments. We determine the start time and end time of segments using change detection followed by a piece-wise linear fit algorithm on joint angle and bone length sequences. Domain-specific symbolic primitives are fit to pose keypoint trajectories of those extracted segments in order to obtain a higher level semantic representation. Since this representation is part-based, it is complementary to the compositional nature of human actions, i.e., a complex activity can be broken down into elementary sub-actions. We show the effectiveness of this representation in the downstream task of class agnostic repetition detection. We propose a repetition counting algorithm based on consecutive similarity matching of primitives, which can do online repetition counting. We also compare the results with a similar but offline repetition counting algorithm. The results of the experiments demonstrate that, despite operating online, the proposed method performs better or on par with the existing method.

DCE-FORMER: A Transformer-based Model With Mutual Information And Frequency-based Loss Functions For Early And Late Response Prediction In Prostate DCE-MRI

Dynamic Contrast Enhanced Magnetic Resonance Imaging aids in the detection and assessment of tumor aggressiveness by using a Gadolinium-based contrast agent (GBCA). However, GBCA is known to have potential toxic effects. This risk can be avoided if we obtain DCE-MRI images without using GBCA. We propose, DCE-former, a transformer-based neural network to generate early and late response prostate DCE-MRI images from non-contrast multimodal inputs (T2 weighted, Apparent Diffusion Coefficient, and T1 pre-contrast MRI). Additionally, we introduce (i) a mutual information loss function to capture the complementary information about contrast uptake, and (ii) a frequency-based loss function in the pixel and Fourier space to learn local and global hyper-intensity patterns in DCE-MRI. Extensive experiments show that DCE-former outperforms other methods with improvement margins of +1.39 dB and +1.19 db in PSNR, +0.068 and +0.055 in SSIM, and -0.012 and -0.013 in Mean Absolute Error for early and late response DCE-MRI, respectively.

Immersive Virtual Reality Platform for Robot-Assisted Antenatal Ultrasound Scanning

Maternal health remains a pervasive challenge in developing and underdeveloped countries. Inadequate access to basic antenatal Ultrasound (US) examinations, limited resources such as primary health services and infrastructure, and lack of skilled healthcare professionals are the major concerns. To improve the quality of maternal care, robot-assisted antenatal US systems with teleoperable and autonomous capabilities were introduced. However, the existing teleoperation systems rely on standard video stream-based approaches that are constrained by limited immersion and scene awareness. Also, there is no prior work on autonomous antenatal robotic US systems that automate standardized scanning protocols. To that end, this paper introduces a novel Virtual Reality (VR) platform for robotic antenatal ultrasound, which enables sonologists to control a robotic arm over a wired network. The effectiveness of the system is enhanced by providing a reconstructed 3D view of the environment and immersing the user in a VR space. Also, the system facilitates a better understanding of the anatomical surfaces to perform pragmatic scans using 3D models. Further, the proposed robotic system also has autonomous capabilities; under the supervision of the sonologist, it can perform the standard six-step approach for obstetric US scanning recommended by the ISUOG. Using a 23-week fetal phantom, the proposed system was demonstrated to technology and academia experts at MEDICA 2022 as a part of the KUKA Innovation Award. The positive feedback from them supports the feasibility of the system. It also gave an insight into the improvisations to be carried out to make it a clinically viable system.

Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors

Many segmentation networks have been proposed for 3D volumetric segmentation of tumors and organs at risk. Hospitals and clinical institutions seek to accelerate and minimize the efforts of specialists in image segmentation. Still, in case of errors generated by these networks, clinicians would have to manually edit the generated segmentation maps. Given a 3D volume and its putative segmentation map, we propose an approach to identify and measure erroneous regions in the segmentation map. Our method can estimate error at any point or node in a 3D mesh generated from a possibly erroneous volumetric segmentation map, serving as a Quality Assurance tool. We propose a graph neural network-based transformer based on the Nodeformer architecture to measure and classify the segmentation errors at any point. We have evaluated our network on a high-resolution micro-CT dataset of the human inner-ear bony labyrinth structure by simulating erroneous 3D segmentation maps. Our network incorporates a convolutional encoder to compute node-centric features from the input micro-CT data, the Nodeformer to learn the latent graph embeddings, and a Multi-Layer Perceptron (MLP) to compute and classify the node-wise errors. Our network achieves a mean absolute error of ~0.042 over other Graph Neural Networks (GNN) and an accuracy of 79.53% over other GNNs in estimating and classifying the node-wise errors, respectively. We also put forth vertex-normal prediction as a custom pretext task for pre-training the CNN encoder to improve the network's overall performance. Qualitative analysis shows the efficiency of our network in correctly classifying errors and reducing misclassifications.

HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstruction

Parallel imaging, a fast MRI technique, involves dynamic adjustments based on the configuration i.e. number, positioning, and sensitivity of the coils with respect to the anatomy under study. Conventional deep learning-based image reconstruction models have to be trained or fine-tuned for each configuration, posing a barrier to clinical translation, given the lack of computational resources and machine learning expertise for clinicians to train models at deployment. Joint training on diverse datasets learns a single weight set that might underfit to deviated configurations. We propose, HyperCoil-Recon, a hypernetwork-based coil configuration task-switching network for multi-coil MRI reconstruction that encodes varying configurations of the numbers of coils in a multi-tasking perspective, posing each configuration as a task. The hypernetworks infer and embed task-specific weights into the reconstruction network, 1) effectively utilizing the contextual knowledge of common and varying image features among the various fields-of-view of the coils, and 2) enabling generality to unseen configurations at test time. Experiments reveal that our approach 1) adapts on the fly to various unseen configurations up to 32 coils when trained on lower numbers (i.e. 7 to 11) of randomly varying coils, and to 120 deviated unseen configurations when trained on 18 configurations in a single model, 2) matches the performance of coil configuration-specific models, and 3) outperforms configuration-invariant models with improvement margins of around 1 dB / 0.03 and 0.3 dB / 0.02 in PSNR / SSIM for knee and brain data. Our code is available at https://github.com/sriprabhar/HyperCoil-Recon

SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction

Transformers have emerged as viable alternatives to convolutional neural networks owing to their ability to learn non-local region relationships in the spatial domain. The self-attention mechanism of the transformer enables transformers to capture long-range dependencies in the images, which might be desirable for accelerated MRI image reconstruction as the effect of undersampling is non-local in the image domain. Despite its computational efficiency, the window-based transformers suffer from restricted receptive fields as the dependencies are limited to within the scope of the image windows. We propose a window-based transformer network that integrates dilated attention mechanism and convolution for accelerated MRI image reconstruction. The proposed network consists of dilated and dense neighborhood attention transformers to enhance the distant neighborhood pixel relationship and introduce depth-wise convolutions within the transformer module to learn low-level translation invariant features for accelerated MRI image reconstruction. The proposed model is trained in a self-supervised manner. We perform extensive experiments for multi-coil MRI acceleration for coronal PD, coronal PDFS and axial T2 contrasts with 4x and 5x under-sampling in self-supervised learning based on k-space splitting. We compare our method against other reconstruction architectures and the parallel domain self-supervised learning baseline. Results show that the proposed model exhibits improvement margins of (i) around 1.40 dB in PSNR and around 0.028 in SSIM on average over other architectures (ii) around 1.44 dB in PSNR and around 0.029 in SSIM over parallel domain self-supervised learning. The code is available at https://github.com/rahul-gs-16/sdlformer.git

Automated Knowledge Modeling for Cancer Clinical Practice Guidelines

Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research. Currently, CPGs are primarily published in a document format that is ill-suited for managing this developing knowledge. A knowledge model of the guidelines document suitable for programmatic interaction is required. This work proposes an automated method for extraction of knowledge from National Comprehensive Cancer Network (NCCN) CPGs in Oncology and generating a structured model containing the retrieved knowledge. The proposed method was tested using two versions of NCCN Non-Small Cell Lung Cancer (NSCLC) CPG to demonstrate the effectiveness in faithful extraction and modeling of knowledge. Three enrichment strategies using Cancer staging information, Unified Medical Language System (UMLS) Metathesaurus & National Cancer Institute thesaurus (NCIt) concepts, and Node classification are also presented to enhance the model towards enabling programmatic traversal and querying of cancer care guidelines. The Node classification was performed using a Support Vector Machine (SVM) model, achieving a classification accuracy of 0.81 with 10-fold cross-validation.

Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworks

Meta-learning has recently been an emerging data-efficient learning technique for various medical imaging operations and has helped advance contemporary deep learning models. Furthermore, meta-learning enhances the knowledge generalization of the imaging tasks by learning both shared and discriminative weights for various configurations of imaging tasks. However, existing meta-learning models attempt to learn a single set of weight initializations of a neural network that might be restrictive for multimodal data. This work aims to develop a multimodal meta-learning model for image reconstruction, which augments meta-learning with evolutionary capabilities to encompass diverse acquisition settings of multimodal data. Our proposed model called KM-MAML (Kernel Modulation-based Multimodal Meta-Learning), has hypernetworks that evolve to generate mode-specific weights. These weights provide the mode-specific inductive bias for multiple modes by re-calibrating each kernel of the base network for image reconstruction via a low-rank kernel modulation operation. We incorporate gradient-based meta-learning (GBML) in the contextual space to update the weights of the hypernetworks for different modes. The hypernetworks and the reconstruction network in the GBML setting provide discriminative mode-specific features and low-level image features, respectively. Experiments on multi-contrast MRI reconstruction show that our model, (i) exhibits superior reconstruction performance over joint training, other meta-learning methods, and context-specific MRI reconstruction methods, and (ii) better adaptation capabilities with improvement margins of 0.5 dB in PSNR and 0.01 in SSIM. Besides, a representation analysis with U-Net shows that kernel modulation infuses 80% of mode-specific representation changes in the high-resolution layers. Our source code is available at https://github.com/sriprabhar/KM-MAML/.