Towards anatomically plausible medical image segmentation: from dense labels to graph structures

Speaker: Enzo Ferrante , CONICET / Universidad Nacional del Litoral

Date: Thursday, October 07, 2021

Time: 1:00 PM to 2:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: 32-D507

Event Type: Seminar

Room Description:

Host: Polina Golland, CSAIL

Contact: Polina Golland, polina@csail.mit.edu

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Reminders to: seminars@csail.mit.edu

Reminder Subject: TALK: Towards anatomically plausible medical image segmentation: from dense labels to graph structures

In this seminar, we will discuss some of our recent works
[1,2,3] on representation learning to improve anatomical plausibility in
biomedical image segmentation. We will see how autoencoders can be used to
learn low-dimensional embeddings of anatomical structures and propose
different ways in which these embeddings can be incorporated into deep
learning models for segmentation and registration. The idea is to
constraint the space of solutions and encourage anatomical plausibility in
the model output. We will also briefly comment on other research lines from
our lab related to domain generalization [4], model calibration [5] and
fairness in biomedical image computing [6].

*[1] Learning deformable registration of medical images with anatomical
constraints
*
Mansilla L, Milone D, *Ferrante E.*
Neural Networks, Elsevier (2020)

*[2] Post-DAE: Anatomically Plausible Segmentation via Post-Processing with
Denoising Autoencoders *
Larrazabal A, Martinez C, Glocker B, *Ferrante E.*
IEEE Transactions on Medical Imaging (2020)

*[3] **Hybrid graph convolutional neural networks for landmark-based
anatomical segmentation *
Gaggion N, Mansilla L, Milone D, *Ferrante E*
MICCAI 2021.

*[4]* *Domain Generalization via Gradient Surgery*

Mansilla L, Echeveste R, Milone D, *Ferrante E*
ICCV 2021

*[5]* *Orthogonal Ensemble Networks for Biomedical Image Segmentation*

Larrazabal A, Martínez C, Dolz J, *Ferrante E*
MICCAI 2021

*[6] Gender imbalance in medical imaging datasets produces biased
classifiers for computer-aided diagnosis
*
Larrazabal A, Nieto N, Peterson V, Milone D, *Ferrante E*
PNAS (2020)

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Impact Areas:

See other events that are part of the Biomedical Imaging and Analysis 2021 - 2022.

Created by Polina Golland Email at Sunday, September 26, 2021 at 4:27 PM.