MLCN2020 proceedings is now available at https://link.springer.com/book/10.1007/978-3-030-66843-3.
Based on the evaluation of the MLCN 2020 scientific committee members on the scientific content, significance of the contribution, and clarity of the communication, the MLCN2020 best paper award is presented to Naresh Nandakumar and his coauthors Niharika S. D’Souza, Komal Manzoor, Jay Pillai, Sachin Gujar, Haris Sair, and Archana Venkataraman for the paper entitled “A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity”.
Naresh is currently a 4th year PhD student in the Neural Systems Analysis lab at the Johns Hopkins University. He received his Bachelor’s from Vanderbilt University, where he did research in the MASI lab. His research interests lie at the intersection of statistical modeling/deep learning and clinical neuroimaging. He has presented at ISBI 2018, MICCAI 2018, MICCAI 2019 and MICCAI 2020. He develops machine learning models to tackle clinically relevant problems. Currently, he is working on eloquent cortex detection of brain tumor patients using resting-state fMRI connectivity. He is always eager to speak with prospective collaborators, you can reach him at firstname.lastname@example.org or on his LinkedIn.
The winner is selected among 18 accepted papers for the presentation at the MLCN 2020. The runner-up papers include:
- Jianyuan Zhang, Feng Shi, Lei Chen, Zhong Xue, Lichi Zhang, and Dahong Qian: “Ischemic Stroke Segmentation from CT Perfusion Scans Using Cluster-Representation Learning“
- Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, and Nils D. Forkert: “Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows“
- Samuel Budd, Prachi Patkee, Ana Baburamani, Mary Rutherford, Emma C. Robinson, Bernhard Kainz: “Surface Agnostic Metrics for Cortical Volume Segmentation and Regression“
- Yannick Suter, Urspeter Knecht, Roland Wiest, Ekkehard Hewer, Philippe Schucht, and Mauricio Reyes: “Towards MRI Progression Features for Glioblastoma Patients: From Automated Volumetry and Classical Radiomics to Deep Feature Learning“
Please make sure you have already prepared:
- Your computer with either Windows or MacOs.
- Your presentation: Please use 16:9 aspect ratio for your slides.
- An internet connection.
- Headphones and a high-quality microphone.
- A webcam.
To record your presentation, using the Zoom platform.
- Download and install the “Zoom Client for Meetings” Software from https://zoom.us/support/download if you haven’t already.
- Double click on “Start Zoom” to launch the application.
- Sign In. If you don’t have an account, click “Sign Up Free”.
- After signing in, you will see the Home tab.
- Click on the gear icon ⚙ in the top-right corner to open the settings.
- In the Video tab, Select the camera you want Zoom to use “16:9 (Widescreen)”. Select “Enable HD”.
- In the audio tab, set your microphone and speaker, test them, and adjust their volume.
- In the recording tab, Verify the location for your local recordings and change it if necessary. Select “Optimize for 3rd party video editor”. Select “Record video during screen sharing”. Select “Place video next to the shared screen in the recording”.
- Close the settings and prepare to start recording. Close all applications except Zoom and your presentation. In the Zoom Client, start a new meeting from the “Home Tab”. Click “Share Screen” in the meeting controls.
- Select your presentation window, select the “Share Computer Sound” checkbox and click the “Share” button on the right. A green border indicates which window you are currently sharing.
- IMPORTANT: Please be sure to maximize the small floating window showing the webcam video to make it as large as possible. Do so by dragging the bottom-left corner of the window as wide as it can go.
NOTE: This setting has a direct impact on the recorded video layout and will have a negative impact on the recording if not set properly.
- While sharing, switch the presentation software into slide show/presentation mode.
- In order to ensure that the webcam video does not overlap with your view of the slides, click in the center of the black bar at the top of the video screen and drag it to the bottom-right corner of your screen. Do not simply minimise this screen as this will affect the recording of you in the final video. Having the webcam video partially off screen will not impact on the recording of you in the final video. The final video will display your slides to one side of the screen and the recording of your webcam to the other.
- Start the recording in the Meeting Control -> More -> Record.
- Give your lecture and please make sure you do not go over your allotted time.
- Once you finish your lecture, end the meeting by clicking on the right-bottom corner red button “END”> “End Meeting for all”. The recording will stop automatically.
- After the meeting has ended, Zoom will convert the recording so you can access the files. If you have trouble finding your recorded video file, return to the Zoom Home tab, select “Meetings” and your recorded files on the left.
- Locate the .mp4 file of the recording and open it.
NOTE: We will edit the beginning and end of the video for you so that it will play only from the start and end of your lecture.
- Review your lecture.
- Are both the video on the right and the presentation on the left visible?
- Is the audio clear?
- Are you happy with the overall lecture?
- If you are happy with your video, please upload the file to the link provided to you in the email regarding these guidelines.
- If you are not happy with your video, please go back to the beginning of this section and record it again.
- Review your lecture.
Title: Impact of AI and deep learning on imaging of neurodegenerative diseases
Abstract: Biomarkers have become increasingly important to understand the biology of neurodegenerative diseases. We now see a paradigm shift recasting the definition of neurodegenerative disease in living people from syndromal to a biological construct. Effective implementation of such biological constructs though requires widespread availability of biomarkers. This talk will address some of the challenges and AI based advances in neuroimaging-based biomarkers for faster, safer, and smarter operationalization of biomarker-based classification, risk assessment, diagnosis, prognosis, and even prediction of therapy responses in neurodegenerative diseases.
Title: AI-enabled Neurology, Dealing with the real world
Abstract: Recent developments in artificial intelligence and the availability of large scale medical imaging datasets allow us to learn how the human brain truly looks like from a biological, physiological, anatomical and pathological point-of-view. This learning process can be further augmented by diagnostic and radiological report data available in clinical systems, providing an integrated view of the human interpretation of medical imaging data. This talk will present how these models can learn from big and unstructured data and then be used as tools for precision medicine, where we aim to translate advanced imaging technologies and biomarkers to clinical practice in order to streamline the clinical workflow and improve the quality of care. This process of technical translation requires deep algorithmic integration into the radiological workflow, fully automated image processing, quality control and assurance, extensive validation on clinical grade data, and the deployment of an automated reporting system that summarizes a complex set of imaging biomarkers, highlighting the presence of abnormalities.