Advances in breast cancer imaging include the latest physical-technical developments, quality control aspects, and clinical experiences with novel breast imaging technologies, including digital mammography, tomosynthesis, breast CT, MR, ultrasound, optical, and molecular imaging for early

This JMI special issue will focus on research in digital mammography, digital breast tomosynthesis, CT, MRI, ultrasound, optical imaging, molecular imaging, pathological imaging, multimodality imaging, theranostics, image processing, image reconstruction, visualization, computer-aided diagnosis and therapy, quantitative imaging, radiomics, deep learning, system design, image quality, observer performance, human factors and workflow, breast density, clinical evaluation, and virtual, in silico, and modeling clinical trials.

This JMI special issue is open to everyone, yet especially encourages relevant submissions from the 17th International Workshop on Breast Imaging (IWBI), recently held at the University of Chicago in 2024. The IWBI is designed as a platform to present the latest technological and machine intelligence developments and clinical experiences of novel breast imaging technologies, bringing together a diverse group of researchers, clinicians, and representatives of industry, who are jointly committed to developing technology for the early detection and subsequent patient management of breast cancer.

Manuscripts should conform to the JMI author guidelines:  http://spie.org/JMIauthorinfo. Prospective authors should submit an electronic copy of their manuscript through the online submission system at https://jmi.msubmit.net. Please indicate in your cover letter that the submission is for this special section.

Each manuscript will be reviewed by at least two independent reviewers. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within six weeks. Special sections are opened for publication once a minimum of four papers have been accepted; each paper is published as soon as the copyedited and typeset proofs are approved by the author.

Medical image perception research measures the human observer's ability to perform specific diagnostic tasks using real or simulated medical images and compares the observer performance with predictions from quantitative models.

The theme of this special issue is image perception and observer performance research on detection and discrimination of abnormalities; cognition, psychophysics, and behavior; perception errors; visual search patterns; human and ideal observer models; computer-based perception; impact of display and ergonomics; image processing; and assessment methods, metrics, and statistics. Although radiology imaging is a key focus, we are particularly interested in articles that deal with other medical imaging specialty applications such as pathology, ophthalmology, dermatology, and telemedicine.

This special issue is open to everyone, and especially encourages relevant submissions from the Medical Image Perception Conference (MIPS XX). The Medical Image Perception Conference is a biennial conference dedicated to bringing together people interested in human and computer perception of medical image information and related subjects, such as detection and discrimination of abnormalities, cognitive and psychophysical processes, perception errors, search patterns, human and ideal observer models, computer-based perception/AI, impact of display and ergonomic factors on image perception and performance, role of image processing on image perception and performance, and assessment methodologies.

Manuscripts should conform to the JMI author guidelines:  http://spie.org/JMIauthorinfo. Prospective authors should submit an electronic copy of their manuscript through the online submission system at https://jmi.msubmit.net. Please indicate in your cover letter that the submission is for this special section.

Each manuscript will be reviewed by at least two independent reviewers. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within six weeks. Special sections are opened for publication once a minimum of four papers have been accepted; each paper is published as soon as the copyedited and typeset proofs are approved by the author.

The development of large-scale pathological image analysis algorithms is essential for enhancing patient outcomes and propelling advancements in healthcare. With the burgeoning availability of big data in computational pathology, there exists a significant opportunity to craft more precise and individualized diagnostic and treatment strategies. However, the complexity and diversity of digital pathological images and their varying modalities present substantial challenges in devising scalable image processing frameworks capable of managing inter-subject variations and delivering robust, timely medical image analysis. At the same time, ethical implementation of the underlying tools is of paramount interest, to ensure addressing model bias, data imbalance, fairness, and transparency, requiring usability and co-design approaches to be integrated with computational pipeline development.

To overcome these hurdles, it is imperative to foster interdisciplinary collaborations among engineers, scientists, clinicians, medical AI ethicists, and human factor researchers. By uniting the expertise of these professionals, innovative methods for data analysis, management, and sharing can be developed to optimize the utilization of big data in computational pathology. Moreover, advancements in large-scale data processing and sophisticated foundation models can assist in discerning patterns and relationships between imaging characteristics and clinical outcomes, thus enhancing the accuracy of diagnoses and tailoring treatment plans more effectively to empower various stakeholders, including clinicians and patients. 

While the directions for computational pathology are shaping up, the development of large-scale pathological image analysis algorithms has not kept pace with the increasing sophistication and complexity of image modalities. It is crucial, therefore, to prioritize the creation of patient-centered clinical image analysis frameworks that incorporate large-scale data, models, and infrastructures. These systems must be robust yet capable of rapid processing to support timely decision-making in clinical settings. There is a pressing need for the development of fast, efficient algorithms that can handle extensive volumes of digital pathologic data. Similarly, the tools developed need to be codesigned with important stakeholders, including patients and clinicians, ensuring providing critical support in clinical decision making and ultimately improving the speed and accuracy of patient care interventions.

This JMI special section welcomes original research papers that develop new methods for computational pathology with large-scale data, models, and infrastructures. We also welcome high-quality submissions from work presented at top conferences, such as MICCAI, IPMI, MIDL, CVPR, ICLR, ICML, and others, that propose new methods and techniques for leveraging patient-centered clinical image analysis with large-scale data, models, and infrastructures. Topics of interest include, but are not limited to, the following:

Computational pathology algorithms

• Patient-centered clinical image analysis with image and non-image data analysis
• Domain adaptation techniques for transferring models across different clinical domains
• Deep learning for pathological image analysis using large datasets
• The development of fast and efficient algorithms that can process large amounts of pathological imaging data in point-of-care decision support
• Multi-omics and texture analysis using digital pathology
• Personalized medicine using digital pathology data
• Big Data analytics for image-guided therapies
• Explainable AI techniques for clinical decision-making using foundation models
• Privacy-preserving techniques for using foundation models in clinical image analysis
• Quality assurance and quality control in digital pathology studies
• Ethical AI development in computational pathology
• Usability and co-design study in to enhance computational pathology pipeline

Clinical digital pathology image analysis with large-scale deep learning models

• Developing models for clinical outcomes using large-scale digital pathology datasets
• Efficient fine-tuning methods for clinical applications using foundation models
• Evaluating and benchmarking foundation models for digital pathology image analysis applications
• Developing foundation models for real-time image analysis in clinical settings
• Adapting foundation models for rare disease diagnosis and treatment planning

Computational Pathology with advanced computational infrastructures

• Developing algorithms for data standardization and harmonization in multi-site studies
• Developing federated learning and distributed computing frameworks for large-scale digital pathology imaging datasets
• Federated learning methods for computational pathology
• The development of fast and efficient infrastructures that can process large amounts of pathological image data in point-of-care decision support
• Developing automated computational pathology pipelines for large-scale medical imaging studies and deployment

Manuscripts should conform to the JMI author guidelines:  http://spie.org/JMIauthorinfo. Prospective authors should submit an electronic copy of their manuscript through the online submission system at https://jmi.msubmit.net. Please indicate in your cover letter that the submission is for this special section.

Each manuscript will be reviewed by at least two independent reviewers. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within six weeks. Special sections are opened for publication once a minimum of four papers have been accepted; each paper is published as soon as the copyedited and typeset proofs are approved by the author.

Theranostics, the combination of the terms therapeutics and diagnostics, is an emerging field of medicine that uses a radiopharmaceutical to identify and locate disease based on specific targets or receptors (diagnosis), followed by a second radiopharmaceutical to deliver therapeutic levels of radiation absorbed dose to target tissue (therapy).  Building upon its foundation in nuclear medicine, more recently the field has gained additional attention because of demonstrations of its ability to improve patient outcomes with low side effects. Successful clinical applications of theranostics include treatments for neuroendocrine tumors and prostate cancer.

Medical imaging plays a vital role in theranostics. In addition to the critical role imaging plays in disease diagnosis, staging, monitoring, and response evaluation, accurate visualization of in vivo radiopharmaceutical biodistribution provides critical information on patient selection for theranostics. Theranostics can incorporate ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), positron-emission tomography (PET), single-photon emission computed tomography (SPECT), and many others. Further, rapid expansion of artificial intelligence (AI) that has revolutionized several aspects of medical imaging is also poised to accelerate advances in theranostics.

This JMI special section welcomes original research papers on theranostics agents, medical imaging methods for theranostics, computational paradigms for dosimetry, dose-response studies, clinical outcomes, applications of artificial intelligence to theranostics, and more. We also welcome high-quality submissions from work presented at top conferences, such as SNMMI, EANM, AAPM, MICCAI, SPIE MI, ISBI, and others.

Topics of interest include, but are not limited to, the following:

• Novel radionuclides in theranostics
• Emerging imaging technologies in theranostics
• Novel theranostics schemas under development
• Imaging biomarkers for tumor characterization
• Methods to identify therapeutic or diagnostic agents
• Prediction of tumor grade and metastatic potential
• Pre-treatment and post-treatment dosimetry in theranostics
• Theranostics methods for cancer prognosis and monitoring
• Multi-modal data modeling methods using imaging, lab data, and health record data
• The role of theranostics in multimodality oncologic care including sequencing and combining therapies
• Theranostics practical aspects in nuclear medicine clinic

Manuscripts should conform to the JMI author guidelines:  http://spie.org/JMIauthorinfo. Prospective authors should submit an electronic copy of their manuscript through the online submission system at https://jmi.msubmit.net. Please indicate in your cover letter that the submission is for this special section.

Each manuscript will be reviewed by at least two independent reviewers. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within six weeks. Special sections are opened for publication once a minimum of four papers have been accepted; each paper is published as soon as the copyedited and typeset proofs are approved by the author.