Welcome to MLMI 2012 - A MICCAI Workshop

Overview

Machine learning plays an essential role in the medical imaging field, including computer-aided diagnosis, image segmentation, image registration, image fusion, image-guided therapy, image annotation and image database retrieval. Machine Learning in Medical Imaging (MLMI 2012) is the third in a series of workshops on this topic in conjunction with MICCAI. This workshop focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging. Our goal is to help advance the scientific research within the broad field of machine learning in medical imaging. The technical program will consist of previously unpublished, contributed, and invited papers. We are looking for original, high-quality submissions on innovative research and development in the analysis of medical image data using machine learning techniques.

Download the conference flyer in PDF format.

Key Dates

• Paper and abstract Submission: June 1, 2012 (Midnight, U.S. Pacific Time)
• Notification of Acceptance:July 10, 2012
• Camera-ready Version: July 24, 2012
• Conference Dates: October 1, 2012

Registration

Please follow this link to the registration site. Make sure to select the MLMI workshop.

Cooperating Organizations

Springer	MICCAI

Call For Papers

Machine learning plays an essential role in the medical imaging field, including computer-aided diagnosis, image segmentation, image registration, image fusion, image-guided therapy, image annotation and image database retrieval. With advances in medical imaging, new imaging modalities and methodologies such as cone-beam/multi-slice CT, 3D ultrasound imaging, tomosynthesis, diffusion-weighted MRI, positron-emission tomography (PET)/CT, electrical impedance tomography and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Single-sample evidence provided by the patient's imaging data is often not sufficient to provide satisfactory performance. Because of large variations and complexity, it is generally difficult to derive analytic solutions or simple equations to represent objects such as lesions and anatomy in medical images. Therefore, tasks in medical imaging require learning from examples for accurate representation of data and prior knowledge.

Researchers are now beginning to use techniques such as modern implementations of supervised, unsupervised, semi-supervised and reinforcement learning, for instance, using probabilistic modeling, manifold learning and kernel methods.

The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. This workshop will focus on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging. We hope the workshop to become a new platform for translating research from bench to bedside. We are looking for original, high-quality submissions on innovative research and development in the analysis of medical image data using machine learning techniques.

Topics of interests include but are not limited to machine learning methods (e.g., support vector machines, statistical methods, manifold-space-based methods, artificial neural networks, extreme learning machines) with their applications to the following areas:

• Medical image analysis (e.g., pattern recognition, classification, segmentation, registration) of anatomical structures and lesions
• Computer-aided detection/diagnosis (e.g., for lung cancer, prostate cancer, breast cancer, colon cancer, brain diseases, liver cancer, acute disease, chronic disease, osteoporosis)
• Multi-modality fusion (e.g., MRI/PET, PET/CT, projection X-ray/CT, X-ray/ultrasound) for diagnosis, image analysis and image guided interventions
• Image reconstruction (e.g., expectation maximization (EM) algorithm, statistical methods, iterative reconstruction) for medical imaging (e.g., CT, PET, MRI, X-ray)
• Image retrieval (e.g., context-based retrieval, lesion similarity)
• Cellular image analysis (e.g., genotype, phenotype, classification, identification, cell tracking)
• Molecular/pathologic image analysis (e.g., PET, digital pathology)
• Dynamic, functional, physiologic, and anatomic imaging

Submission and Presentation Guideline

Proceedings: The MLMI 2012 proceedings will be published as a volume in the Springer Lecture Notes in Computer Science (LNCS) series.

Paper Formatting: Papers are limited to eight pages. Papers should be formatted in Lecture Notes in Computer Science style. Style files can be found on the Springer website. The file format for submissions is Adobe Portable Document Format (PDF). Other formats will not be accepted.

Blind review: MLMI reviewing is double blind: authors do not know the names of the reviewers of their papers, and reviewers do not know the names of the authors. Please see the Anonymity guidelines of MICCAI 2012 for detailed explanations of how to ensure this.

Submission: MLMI is using an online submission system.

Supplemental material: Supplemental material submission is optional. This material may include: videos of results that cannot be included in the main paper, anonymized related submissions to other conferences and journals, and appendices or technical reports containing extended proofs and mathematical derivations that are not essential for understanding of the paper. Contents of the supplemental material should be referred to appropriately in the paper and that reviewers are not obliged to look at it.

Simultaneous submissions: Our policy is that in submitting a paper, authors implicitly acknowledge that NO paper of substantially similar content has been or will be submitted to another conference or workshop until MLMI decisions are made.

Camera-ready Submission MLMI is using the same site for the final submission. Please follow the instructions from the Springer website to prepare your final submission. Authors of each accepted paper needs to upload a single zip file including the following materials: 1. a completed copyright form, 2. a PDF of the camera ready, and 3. source files of the camera ready, namely, a Word file or a .tex file plus all figures, style files, special fonts, .eps, .bib etc.

Past Workshops

First International Workshop on Machine Learning in Medical Imaging (MLMI 2010), Beijing, China, September 20, 2010

Second International Workshop on Machine Learning in Medical Imaging (MLMI 2011), Toronto, Canada, September 18, 2011