Author name cluster

Jinman Kim

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17 papers

2 author rows

JBHI Journal 2026 Journal Article

Hierarchical Deep Decision Tree-Based Network for Odontogenic Cystic Lesion Classification in CBCT Images

Zimo Huang
Hao Wang
Bo Li
Eduardo Delamare
Shengfu Huang
Lei Bi
Jinman Kim

Odontogenic cystic lesions (OCLs) are complex jaw abnormalities that require a precise diagnosis of the disease for treatment. Visual OCL diagnosis is commonly based on reviewing cone-beam computed tomography (CBCT) to identify morpho-pathological features associated with specific lesion types in a hierarchical manner. Current state-of-the-art methods focus on extracting features from the image without any guidance beyond the lesion diagnosis, and do not fully leverage the hierarchical relationship between the lesion diagnosis and morphological features. In this study, we propose a hierarchical deep decision tree network (H2DT-Net) with three modules: a deep decision tree-based hierarchical learning module (DHLM) to leverage inter-categorical relationships; a feature category embedding module (FCEM) to capture representations from both diagnostic and morpho-pathological domains and support the DHLM; and a lesion localised attention module (LLAM) to facilitate the feature extraction process by generating lesion-focused attention maps. Evaluated on 289 CBCT images, H2DT-Net achieved state-of-the-art performance in OCL classification. We further demonstrate that our method is effective in clinical settings, where it outperformed six maxillofacial clinicians in diagnostic assessment.

ECAI Conference 2025 Conference Paper

Enhancing Zero-Shot Learning of Pathology Vision-Language Foundation Models in Tumour Malignancy Recognition

Xiao Wang
Usman Naseem
Jinman Kim

Vision-Language Foundation Models (VLFMs) demonstrate promise in zero-shot learning through joint visual-textual representations. However, in histopathology image analysis, their effectiveness is limited by weak image-text alignment due to coarse-grained textual descriptions that fail to capture critical fine-grained visual details. This misalignment introduces semantic noise and imprecision in zero-shot retrieval, hindering the identification of relevant cases and degrading downstream classification. To address this, we introduce Retrieval-based De-noising Causal Language Modelling (RDCLM), a novel framework that refines noisy retrieval outputs from pathology VLFMs. RDCLM constructs a pathology-specific knowledge base of fine-grained, discriminative tumour malignancy descriptions using a large language model (LLM). Given a query histopathology image, a pathology VLFM retrieves candidate descriptions from this knowledge base. Our de-noising module, leveraging a frozen language model, integrates visual features with these retrieved texts, filtering irrelevant content and enhancing semantic alignment. This significantly improves retrieval precision (by an average of 10% across datasets) and enables more accurate zero-shot image classification. To further bolster performance and generalization, we propose two retrieval augmentation strategies: Retrieval Negatives Replacement (RNR) and Description-wise Shuffling (DS). Extensive evaluations across four histopathology cancer datasets demonstrate that RDCLM significantly outperforms state-of-the-art methods in both zero-shot image-text retrieval and malignancy classification, achieving an average improvement of 12. 7% in F1-score and 9. 6% in accuracy over the second-best competitor. These results highlight the importance of retrieval de-noising for advancing VLFM-based zero-shot learning in histopathology. Our code available at: https: //github. com/xw18958/RDCLM

JBHI Journal 2025 Journal Article

Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging

Yusheng Zhou
Hao Li
Jianan Liu
Zhengmin Kong
Tao Huang
Euijoon Ahn
Zhihan Lv
Jinman Kim

Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for motion artifact reduction (MAR). One disadvantage of these methods is their dependency on acquiring paired sets of motion artifact-corrupted (MA-corrupted) and motion artifact-free (MA-free) MR images for training purposes. Obtaining such image pairs is difficult and therefore limits the application of supervised training. In this paper, we propose a novel UNsupervised Abnormality Extraction Network (UNAEN) to alleviate this problem. Our network is capable of working with unpaired MA-corrupted and MA-free images. It converts the MA-corrupted images to MA-reduced images by extracting abnormalities from the MA-corrupted images using a proposed artifact extractor, which intercepts the residual artifact maps from the MA-corrupted MR images explicitly, and a reconstructor to restore the original input from the MA-reduced images. The performance of UNAEN was assessed by experimenting with various publicly available MRI datasets and comparing them with state-of-the-art methods. The quantitative evaluation demonstrates the superiority of UNAEN over alternative MAR methods and visually exhibits fewer residual artifacts. Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies.

JBHI Journal 2025 Journal Article

Head-Mounted Displays in Context-Aware Systems for Open Surgery: A State-of-the-Art Review

Mingxiao Tu
Hoijoon Jung
Jinman Kim
Andre Kyme

Surgical context-aware systems (SCAS), which leverage real-time data and analysis from the operating room to inform surgical activities, can be enhanced through the integration of head-mounted displays (HMDs). Rather than user-agnostic data derived from conventional, and often static, external sensors, HMD-based SCAS relies on dynamic user-centric sensing of the surgical context. The analyzed context-aware information is then augmented directly into a user's field of view via augmented reality (AR) to directly improve their task and decision-making capability. This state-of-the-art review complements previous reviews by exploring the advancement of HMD- based SCAS, including their development and impact on enhancing situational awareness and surgical outcomes in the operating room. The survey demonstrates that this technology can mitigate risks associated with gaps in surgical expertise, increase procedural efficiency, and improve patient outcomes. We also highlight key limitations still to be addressed by the research community, including improving prediction accuracy, robustly handling data heterogeneity, and reducing system latency.

JBHI Journal 2024 Journal Article

K-PathVQA: Knowledge-Aware Multimodal Representation for Pathology Visual Question Answering

Usman Naseem
Matloob Khushi
Adam G. Dunn
Jinman Kim

Pathology imaging is routinely used to detect the underlying effects and causes of diseases or injuries. Pathology visual question answering (PathVQA) aims to enable computers to answer questions about clinical visual findings from pathology images. Prior work on PathVQA has focused on directly analyzing the image content using conventional pretrained encoders without utilizing relevant external information when the image content is inadequate. In this article, we present a knowledge-driven PathVQA (K-PathVQA), which uses a medical knowledge graph (KG) from a complementary external structured knowledge base to infer answers for the PathVQA task. K-PathVQA improves the question representation with external medical knowledge and then aggregates vision, language, and knowledge embeddings to learn a joint knowledge-image-question representation. Our experiments using a publicly available PathVQA dataset showed that our K-PathVQA outperformed the best baseline method with an increase of 4. 15% in accuracy for the overall task, an increase of 4. 40% in open-ended question type and an absolute increase of 1. 03% in closed-ended question types. Ablation testing shows the impact of each of the contributions. Generalizability of the method is demonstrated with a separate medical VQA dataset.

JBHI Journal 2023 Journal Article

Vision-Language Transformer for Interpretable Pathology Visual Question Answering

Usman Naseem
Matloob Khushi
Jinman Kim

Pathology visual question answering (PathVQA) attempts to answer a medical question posed by pathology images. Despite its great potential in healthcare, it is not widely adopted because it requires interactions on both the image (vision) and question (language) to generate an answer. Existing methods focused on treating vision and language features independently, which were unable to capture the high and low-level interactions that are required for VQA. Further, these methods failed to offer capabilities to interpret the retrieved answers, which are obscure to humans where the models’ interpretability to justify the retrieved answers has remained largely unexplored. Motivated by these limitations, we introduce a vision-language transformer that embeds vision (images) and language (questions) features for an interpretable PathVQA. We present an interpretable tra nsformer-based P ath- VQA (TraP-VQA), where we embed transformers’ encoder layers with vision and language features extracted using pre-trained CNN and domain-specific language model (LM), respectively. A decoder layer is then embedded to upsample the encoded features for the final prediction for PathVQA. Our experiments showed that our TraP-VQA outperformed the state-of-the-art comparative methods with public PathVQA dataset. Our experiments validated the robustness of our model on another medical VQA dataset, and the ablation study demonstrated the capability of our integrated transformer-based vision-language model for PathVQA. Finally, we present the visualization results of both text and images, which explain the reason for a retrieved answer in PathVQA.

AIIM Journal 2022 Journal Article

Deep multi-scale resemblance network for the sub-class differentiation of adrenal masses on computed tomography images

Lei Bi
Jinman Kim
Tingwei Su
Michael Fulham
David Dagan Feng
Guang Ning

Objective The accurate classification of mass lesions in the adrenal glands (‘adrenal masses’), detected with computed tomography (CT), is important for diagnosis and patient management. Adrenal masses can be benign or malignant and benign masses have varying prevalence. Classification methods based on convolutional neural networks (CNNs) are the state-of-the-art in maximizing inter-class differences in large medical imaging training datasets. The application of CNNs, to adrenal masses is challenging due to large intra-class variations, large inter-class similarities and imbalanced training data due to the size of the mass lesions. Methods We developed a deep multi-scale resemblance network (DMRN) to overcome these limitations and leveraged paired CNNs to evaluate the intra-class similarities. We used multi-scale feature embedding to improve the inter-class separability by iteratively combining complementary information produced at different scales of the input to create structured feature descriptors. We augmented the training data with randomly sampled paired adrenal masses to reduce the influence of imbalanced training data. Results We used 229 CT scans of patients with adrenal masses for evaluation. In a five-fold cross-validation, our method had the best results (89. 52 % in accuracy) when compared to the state-of-the-art methods (p < 0. 05). We conducted a generalizability analysis of our method on the ImageCLEF 2016 competition dataset for medical subfigure classification, which consists of a training set of 6776 images and a test set of 4166 images across 30 classes. Our method achieved better classification performance (85. 90 % in accuracy) when compared to the existing methods and was competitive when compared with methods that require additional training data (1. 47 % lower in accuracy). Conclusion Our DMRN sub-classified adrenal masses on CT and was superior to state-of-the-art approaches.

JBHI Journal 2022 Journal Article

DeepMTS: Deep Multi-Task Learning for Survival Prediction in Patients With Advanced Nasopharyngeal Carcinoma Using Pretreatment PET/CT

Mingyuan Meng
Bingxin Gu
Lei Bi
Shaoli Song
David Dagan Feng
Jinman Kim

Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients, as it provides early prognostic information to plan treatments. Recently, deep survival models based on deep learning have demonstrated the potential to outperform traditional radiomics-based survival prediction models. Deep survival models usually use image patches covering the whole target regions (e. g. , nasopharynx for NPC) or containing only segmented tumor regions as the input. However, the models using the whole target regions will also include non-relevant background information, while the models using segmented tumor regions will disregard potentially prognostic information existing out of primary tumors (e. g. , local lymph node metastasis and adjacent tissue invasion). In this study, we propose a 3D end-to-end Deep Multi-Task Survival model (DeepMTS) for joint survival prediction and tumor segmentation in advanced NPC from pretreatment PET/CT. Our novelty is the introduction of a hard-sharing segmentation backbone to guide the extraction of local features related to the primary tumors, which reduces the interference from non-relevant background information. In addition, we also introduce a cascaded survival network to capture the prognostic information existing out of primary tumors and further leverage the global tumor information (e. g. , tumor size, shape, and locations) derived from the segmentation backbone. Our experiments with two clinical datasets demonstrate that our DeepMTS can consistently outperform traditional radiomics-based survival prediction models and existing deep survival models.

YNIMG Journal 2022 Journal Article

Enhancing medical image registration via appearance adjustment networks

Mingyuan Meng
Lei Bi
Michael Fulham
David Dagan Feng
Jinman Kim

Deformable image registration is fundamental for many medical image analyses. A key obstacle for accurate image registration lies in image appearance variations such as the variations in texture, intensities, and noise. These variations are readily apparent in medical images, especially in brain images where registration is frequently used. Recently, deep learning-based registration methods (DLRs), using deep neural networks, have shown computational efficiency that is several orders of magnitude faster than traditional optimization-based registration methods (ORs). DLRs rely on a globally optimized network that is trained with a set of training samples to achieve faster registration. DLRs tend, however, to disregard the target-pair-specific optimization inherent in ORs and thus have degraded adaptability to variations in testing samples. This limitation is severe for registering medical images with large appearance variations, especially since few existing DLRs explicitly take into account appearance variations. In this study, we propose an Appearance Adjustment Network (AAN) to enhance the adaptability of DLRs to appearance variations. Our AAN, when integrated into a DLR, provides appearance transformations to reduce the appearance variations during registration. In addition, we propose an anatomy-constrained loss function through which our AAN generates anatomy-preserving transformations. Our AAN has been purposely designed to be readily inserted into a wide range of DLRs and can be trained cooperatively in an unsupervised and end-to-end manner. We evaluated our AAN with three state-of-the-art DLRs - Voxelmorph (VM), Diffeomorphic Voxelmorph (DifVM), and Laplacian Pyramid Image Registration Network (LapIRN) - on three well-established public datasets of 3D brain magnetic resonance imaging (MRI) - IBSR18, Mindboggle101, and LPBA40. The results show that our AAN consistently improved existing DLRs and outperformed state-of-the-art ORs on registration accuracy, while adding a fractional computational load to existing DLRs.

JBHI Journal 2022 Journal Article

Improving Breast Tumor Segmentation in PET via Attentive Transformation Based Normalization

Xiaoya Qiao
Chunjuan Jiang
Panli Li
Yuan Yuan
Qinglong Zeng
Lei Bi
Shaoli Song
Jinman Kim

Positron Emission Tomography (PET) has become a preferred imaging modality for cancer diagnosis, radiotherapy planning, and treatment responses monitoring. Accurate and automatic tumor segmentation is the fundamental requirement for these clinical applications. Deep convolutional neural networks have become the state-of-the-art in PET tumor segmentation. The normalization process is one of the key components for accelerating network training and improving the performance of the network. However, existing normalization methods either introduce batch noise into the instance PET image by calculating statistics on batch level or introduce background noise into every single pixel by sharing the same learnable parameters spatially. In this paper, we proposed an attentive transformation (AT)-based normalization method for PET tumor segmentation. We exploit the distinguishability of breast tumor in PET images and dynamically generate dedicated and pixel-dependent learnable parameters in normalization via the transformation on a combination of channel-wise and spatial-wise attentive responses. The attentive learnable parameters allow to re-calibrate features pixel-by-pixel to focus on the high-uptake area while attenuating the background noise of PET images. Our experimental results on two real clinical datasets show that the AT-based normalization method improves breast tumor segmentation performance when compared with the existing normalization methods.

AAAI Conference 2022 Conference Paper

Self-Supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss

Hao Wang
Euijoon Ahn
Jinman Kim

Recent advances in supervised deep learning methods are enabling remote measurements of photoplethysmographybased physiological signals using facial videos. The performance of these supervised methods, however, are dependent on the availability of large labelled data. Contrastive learning as a self-supervised method has recently achieved state-of-the-art performances in learning representative data features by maximising mutual information between different augmented views. However, existing data augmentation techniques for contrastive learning are not designed to learn physiological signals from videos and often fail when there are complicated noise and subtle and periodic colour/shape variations between video frames. To address these problems, we present a novel self-supervised spatiotemporal learning framework for remote physiological signal representation learning, where there is a lack of labelled training data. Firstly, we propose a landmark-based spatial augmentation that splits the face into several informative parts based on the Shafer’s dichromatic reflection model to characterise subtle skin colour fluctuations. We also formulate a sparsitybased temporal augmentation exploiting Nyquist–Shannon sampling theorem to effectively capture periodic temporal changes by modelling physiological signal features. Furthermore, we introduce a constrained spatiotemporal loss which generates pseudo-labels for augmented video clips. It is used to regulate the training process and handle complicated noise. We evaluated our framework on 3 public datasets and demonstrated superior performances than other self-supervised methods and achieved competitive accuracy compared to the state-of-the-art supervised methods. Code is available at https: //github. com/Dylan-H-Wang/SLF-RPM.

JBHI Journal 2021 Journal Article

Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation

Xiaohang Fu
Lei Bi
Ashnil Kumar
Michael Fulham
Jinman Kim

Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of PET and anatomical information from CT. Tumor segmentation is a critical element of PET-CT but at present, the performance of existing automated methods for this challenging task is low. Segmentation tends to be done manually by different imaging experts, which is labor-intensive and prone to errors and inconsistency. Previous automated segmentation methods largely focused on fusing information that is extracted separately from the PET and CT modalities, with the underlying assumption that each modality contains complementary information. However, these methods do not fully exploit the high PET tumor sensitivity that can guide the segmentation. We introduce a deep learning-based framework in multimodal PET-CT segmentation with a multimodal spatial attention module (MSAM). The MSAM automatically learns to emphasize regions (spatial areas) related to tumors and suppress normal regions with physiologic high-uptake from the PET input. The resulting spatial attention maps are subsequently employed to target a convolutional neural network (CNN) backbone for segmentation of areas with higher tumor likelihood from the CT image. Our experimental results on two clinical PET-CT datasets of non-small cell lung cancer (NSCLC) and soft tissue sarcoma (STS) validate the effectiveness of our framework in these different cancer types. We show that our MSAM, with a conventional U-Net backbone, surpasses the state-of-the-art lung tumor segmentation approach by a margin of 7. 6% in Dice similarity coefficient (DSC).

JBHI Journal 2017 Journal Article

An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification

Ashnil Kumar
Jinman Kim
David Lyndon
Michael Fulham
Dagan Feng

The availability of medical imaging data from clinical archives, research literature, and clinical manuals, coupled with recent advances in computer vision offer the opportunity for image-based diagnosis, teaching, and biomedical research. However, the content and semantics of an image can vary depending on its modality and as such the identification of image modality is an important preliminary step. The key challenge for automatically classifying the modality of a medical image is due to the visual characteristics of different modalities: some are visually distinct while others may have only subtle differences. This challenge is compounded by variations in the appearance of images based on the diseases depicted and a lack of sufficient training data for some modalities. In this paper, we introduce a new method for classifying medical images that uses an ensemble of different convolutional neural network (CNN) architectures. CNNs are a state-of-the-art image classification technique that learns the optimal image features for a given classification task. We hypothesise that different CNN architectures learn different levels of semantic image representation and thus an ensemble of CNNs will enable higher quality features to be extracted. Our method develops a new feature extractor by fine-tuning CNNs that have been initialized on a large dataset of natural images. The fine-tuning process leverages the generic image features from natural images that are fundamental for all images and optimizes them for the variety of medical imaging modalities. These features are used to train numerous multiclass classifiers whose posterior probabilities are fused to predict the modalities of unseen images. Our experiments on the ImageCLEF 2016 medical image public dataset (30 modalities; 6776 training images, and 4166 test images) show that our ensemble of fine-tuned CNNs achieves a higher accuracy than established CNNs. Our ensemble also achieves a higher accuracy than methods in the literature evaluated on the same benchmark dataset and is only overtaken by those methods that source additional training data.

JBHI Journal 2017 Journal Article

Automatic Measurement of Thalamic Diameter in 2-D Fetal Ultrasound Brain Images Using Shape Prior Constrained Regularized Level Sets

Pradeeba Sridar
Ashnil Kumar
Changyang Li
Joyce Woo
Ann Quinton
Ron Benzie
Michael J. Peek
Dagan Feng

We derived an automated algorithm for accurately measuring the thalamic diameter from 2-D fetal ultrasound (US) brain images. The algorithm overcomes the inherent limitations of the US image modality: nonuniform density; missing boundaries; and strong speckle noise. We introduced a “guitar” structure that represents the negative space surrounding the thalamic regions. The guitar acts as a landmark for deriving the widest points of the thalamus even when its boundaries are not identifiable. We augmented a generalized level-set framework with a shape prior and constraints derived from statistical shape models of the guitars; this framework was used to segment US images and measure the thalamic diameter. Our segmentation method achieved a higher mean Dice similarity coefficient, Hausdorff distance, specificity, and reduced contour leakage when compared to other well-established methods. The automatic thalamic diameter measurement had an interobserver variability of -0. 56 ± 2. 29 mm compared to manual measurement by an expert sonographer. Our method was capable of automatically estimating the thalamic diameter, with the measurement accuracy on par with clinical assessment. Our method can be used as part of computer-assisted screening tools that automatically measure the biometrics of the fetal thalamus; these biometrics are linked to neurodevelopmental outcomes.

JBHI Journal 2017 Journal Article

Occlusion and Slice-Based Volume Rendering Augmentation for PET-CT

Younhyun Jung
Jinman Kim
Dagan Feng
Michael Fulham

Dual-modality positron emission tomography and computed tomography (PET-CT) depicts pathophysiological function with PET in an anatomical context provided by CT. Three-dimensional volume rendering approaches enable visualization of a two-dimensional slice of interest (SOI) from PET combined with direct volume rendering (DVR) from CT. However, because DVR depicts the whole volume, it may occlude a region of interest, such as a tumor in the SOI. Volume clipping can eliminate this occlusion by cutting away parts of the volume, but it requires intensive user involvement in deciding on the appropriate depth to clip. Transfer functions that are currently available can make the regions of interest visible, but this often requires complex parameter tuning and coupled preprocessing of the data to define the regions. Hence, we propose a new visualization algorithm where an SOI from PET is augmented by volumetric contextual information from a DVR of the counterpart CT so that the obtrusiveness from the CT in the SOI is minimized. Our approach automatically calculates an augmentation depth parameter by considering the occlusion information derived from the voxels of the CT in front of the PET SOI. The depth parameter is then used to generate an opacity weight function that controls the amount of contextual information visible from the DVR. We outline the improvements with our visualization approach compared to other slice-based and our previous approaches. We present the preliminary clinical evaluation of our visualization in a series of PET-CT studies from patients with nonsmall cell lung cancer.

JBHI Journal 2017 Journal Article

Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images

Euijoon Ahn
Jinman Kim
Lei Bi
Ashnil Kumar
Changyang Li
Michael Fulham
David Dagan Feng

The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in underand oversegmentation of the skin lesion. We suggest that saliency detection using the reconstruction errors derived from a sparse representation model coupled with a novel background detection can more accurately discriminate the lesion from surrounding regions. We further propose a Bayesian framework that better delineates the shape and boundaries of the lesion. We also evaluated our approach on two public datasets comprising 1100 dermoscopic images and compared it to other conventional and state-of-the-art unsupervised (i. e. , no training required) lesion segmentation methods, as well as the state-of-the-art unsupervised saliency detection methods. Our results show that our approach is more accurate and robust in segmenting lesions compared to other methods. We also discuss the general extension of our framework as a saliency optimization algorithm for lesion segmentation.

JBHI Journal 2015 Journal Article

A Visual Analytics Approach Using the Exploration of Multidimensional Feature Spaces for Content-Based Medical Image Retrieval

Ashnil Kumar
Falk Nette
Karsten Klein
Michael Fulham
Jinman Kim

Content-based image retrieval (CBIR) is a search technique based on the similarity of visual features and has demonstrated potential benefits for medical diagnosis, education, and research. However, clinical adoption of CBIR is partially hindered by the difference between the computed image similarity and the user's search intent, the semantic gap, with the end result that relevant images with outlier features may not be retrieved. Furthermore, most CBIR algorithms do not provide intuitive explanations as to why the retrieved images were considered similar to the query (e. g. , which subset of features were similar), hence, it is difficult for users to verify if relevant images, with a small subset of outlier features, were missed. Users, therefore, resort to examining irrelevant images and there are limited opportunities to discover these “missed” images. In this paper, we propose a new approach to medical CBIR by enabling a guided visual exploration of the search space through a tool, called visual analytics for medical image retrieval (VAMIR). The visual analytics approach facilitates interactive exploration of the entire dataset using the query image as a point-of-reference. We conducted a user study and several case studies to demonstrate the capabilities of VAMIR in the retrieval of computed tomography images and multimodality positron emission tomography and computed tomography images.