10:35 a.m.
*Title:
3D Lung Cancer Segmentation and Representation Learning for Few-Shot
Classification
*Abstract:
The recent advances in Deep Learning made many tasks in Computer Vision
much easier to tackle. However, working with a small amount of data, and
highly imbalanced real-world datasets can still be very challenging. In
this talk, I will present two of my recent projects, where modelling and
training occur under those circumstances. Firstly, I will introduce a novel
3D UNet-like model for fast volumetric segmentation of lung cancer nodules
in Computed Tomography (CT) imagery. This model highly relied on kernel
factorisation and other architectural improvements to reduce the number of
parameters and computational load, allowing its successful use in
production. Secondly, I will discuss the use of representation learning or
similarity metric learning for few-shot classification tasks, and more
specifically its use in a competition at NeurIPS 2019 and Kaggle. This
competition aimed to detect the effects of over 1000 different genetic
treatments to 4 types of human cells, and published a dataset composed of
6-channel fluorescent microscopy images with only a handful of samples per
target class.
*Speaker:
Henrique Mendonça got a MSc at the University of Zurich in 2015 and has 10+
years experience in designing and developing systems in areas from
real-time embedded systems to high performance distributed applications,
computer vision and machine learning.
*Date:
Friday, 14th of February 2020, 12:30-13:30
*Location:
Manno, Galleria 1, 2nd floor, room TBA
*Doodle registration:
Pizza (or alternative food) and drinks will be offered at the end of the
talk. If you plan to attend the lunch, please register in a timely fashion
(meaning at the latest at 9AM of the same day of the talk) at the following
link so that we will have no shortage of food.
https://doodle.com/poll/cdqq4ygs56qzrkpb
12:33 p.m.
New subject: [Talks@IDSIA] PIDSIA Seminar by Henrique Mendonça, Friday 14 Feb 12:30
Errata: the talk will take place as announced but it will NOT be a PIDSIA
-- I deleted the doodle accordingly.
Those interested to have (not free) lunch with the speaker can join us at
La Brace just after the talk!
Cheers,
Alessandro
On Wed, Feb 12, 2020 at 10:40 AM Announcements of talks at IDSIA <
talks(a)mailman2.ti-edu.ch> wrote:
*Title:
3D Lung Cancer Segmentation and Representation Learning for Few-Shot
Classification
*Abstract:
The recent advances in Deep Learning made many tasks in Computer Vision
much easier to tackle. However, working with a small amount of data, and
highly imbalanced real-world datasets can still be very challenging. In
this talk, I will present two of my recent projects, where modelling and
training occur under those circumstances. Firstly, I will introduce a novel
3D UNet-like model for fast volumetric segmentation of lung cancer nodules
in Computed Tomography (CT) imagery. This model highly relied on kernel
factorisation and other architectural improvements to reduce the number of
parameters and computational load, allowing its successful use in
production. Secondly, I will discuss the use of representation learning or
similarity metric learning for few-shot classification tasks, and more
specifically its use in a competition at NeurIPS 2019 and Kaggle. This
competition aimed to detect the effects of over 1000 different genetic
treatments to 4 types of human cells, and published a dataset composed of
6-channel fluorescent microscopy images with only a handful of samples per
target class.
*Speaker:
Henrique Mendonça got a MSc at the University of Zurich in 2015 and has
10+ years experience in designing and developing systems in areas from
real-time embedded systems to high performance distributed applications,
computer vision and machine learning.
*Date:
Friday, 14th of February 2020, 12:30-13:30
*Location:
Manno, Galleria 1, 2nd floor, room TBA
*Doodle registration:
Pizza (or alternative food) and drinks will be offered at the end of the
talk. If you plan to attend the lunch, please register in a timely fashion
(meaning at the latest at 9AM of the same day of the talk) at the following
link so that we will have no shortage of food.
https://doodle.com/poll/cdqq4ygs56qzrkpb
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