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Domain Adaptation for Microscopy Imaging

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Learning and leveraging shared domain semantics to counteract visual domain shifts

Róger Bermúdez Chacón

One of the main limitations of artificial intelligence today is its inability to adapt to unforeseen circumstances. Machine Learning (ML), due to its data-driven nature, is particularly susceptible to this. ML relies on observations in order to learn impli ...

EPFL2020

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The difficulty of obtaining annotations to build training databases still slows down the adoption of recent deep learning approaches for biomedical image analysis. In this paper, we show that we can train a Deep Net to perform 3D volumetric delineation giv ...

ELSEVIER2020

DeepFocus: a Few-shot Microscope Slide Auto-Focus using a Sample Invariant CNN-based Sharpness Function

Michael Stefan Daniel Liebling, Adrian Shajkofci

Autofocus (AF) methods are extensively used in biomicroscopy, for example to acquire timelapses, where the imaged objects tend to drift out of focus. AF algorithms determine an optimal distance by which to move the sample back into the focal plane. Current ...

2020

Image Matching Across Wide Baselines: From Paper to Practice

Pascal Fua, Kwang Moo Yi, Eduard Trulls Fortuny, Jiri Matas, Anastasiia Mishchuk

We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task-the accuracy of the reconstructed camera pose-as our primary metric. Our pipeline's modular structure allows easy integration, confi ...

2020

Microscopy of the beating heart in embryos provides key insights for the study of its development. However, achieving a sufficiently high framerate is difficult with conventional cameras. Here, we present a method to reconstruct an image sequence covering ...

Idiap2019

This report presents the software tools that will be available online to help users accurately segment ultra-thin sections of brain tissue in a large image to determine the section’s coordinates. In order to predict these coordinates, the goal was to use a ...

2019

Mean-Field methods for Structured Deep-Learning in Computer Vision

Pierre Bruno Baqué

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Bugra Tekin

Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growin ...

EPFL2018

Compact lensless digital holography

Manon Rostykus

Microscopy is of high interest for biology since it allows imaging features that are too small to be seen with naked eyes. However, cells are mostly transparent to visible and infrared light which makes it difficult to see with a traditional microscope. To ...

EPFL2018

A Domain-Adaptive Two-Stream U-Net for Electron Microscopy Image Segmentation

Pascal Fua, Mathieu Salzmann, Pablo Marquez Neila, Róger Bermúdez Chacón

Deep networks such as the U-Net are outstanding at segmenting biomedical images when enough training data is available, but only then. Here we introduce a Domain Adaptation approach that relies on two coupled U-Nets that either regularize or share correspo ...

2018