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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 The technical program will consist of previously unpublished, contributed, and invited papers.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.

Presentation Guideline

Each oral paper gets 18 minutes (15+3) of presentation time. The poster size for MICCAI 2012 workshops is standard A0 Portrait ( 841mm in width and 1189mm in height). Please prepare your posters accordingly. Posters have to be mounted on the workshop day from 8:30 AM and will have withdrawn before 6:00 PM on the same day. Non-withdrawn posters will be discarded.

Key Dates

  • Paper and Abstract Submission: June 1, 2012 June 17, 2012
  • Notification of Acceptance: July 6, 2012
  • Camera-ready Version: July 24, 2012
  • Conference Date: October 1, 2012

Workshop Chairs

Registration

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

Cooperating Organizations

Springer MICCAI
LNCS Machine Vision Applications

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.

Workshop Summary

Proceedings: The MLMI 2012 proceedings has been published as a volume (vol. 7588), Machine Learning in Medical Imaging, in the Springer Lecture Notes in Computer Science (LNCS) series.

Statistics: A total of 67 papers were submitted to the workshop in response to the call for papers.  Each of the 67 papers underwent a rigorous double-blinded peer-review process, with each paper being reviewed by at least two (mostly three) external reviewers in the program committee composed of 51 known experts in the field.  Based on the reviewing scores and critics, a total of the 33 best papers (49%) were accepted and included in the Springer LNCS volume as well as the MICCAI Workshop Proceedings.  Among them, 15 papers (22%) were selected for oral presentations.  We would like to thank the 51 program committee members for their outstanding reviews.

The Best Paper Award: The MLMI 2012 presented the Best Paper Award to Xinghua Lou, Luca Fiaschi, Ullrich Koethe and Fred Hamprecht in recognition of excellence of their paper entitled "Quality Classification of Microscopic Imagery with Weakly Supervised Learning, "  The rigorous selection process is summarized as follows:  Papers with an average score greater than or equal to 4.0 (out of 5.0) given by three reviewers on the program committee in the double-blinded review process were selected as award candidates.   Organizers reviewed and rated final submitted papers of the candidates before the workshop as well as their presentations including questions and answers during the workshop.  The final decision was made by taking both paper and presentation qualities into account.  Since all papers were outstanding, it was not an easy decision.

Attendance: There were more than 150 participants throughout the workshop.  Prof. Rangarajan gave an invited talk titled "Boosted Segmentation of Neuroanatomical Datasets using Shape Complex Atlases" that covered key topics, methods and examples of "learning and discovery of clinically useful information from medical images" in the field of machine learning in medical imaging.  We would like to thank Prof. Rangarajan for his insightful talk.  Speakers were energetic in their presentations.  Attendees actively participated in question and answer sessions.  Poster session was crowded with interested audience, and there were enthusiastic discussions.  We would like to thank all the presenters for their excellent research and presentations and all the attendees for their active participation.  We are also grateful to session chairs for their outstanding presiding.

Photos: Please enjoy some photos taken during the workshop.

 

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