22 research outputs found
Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
Large, fine-grained image segmentation datasets, annotated at pixel-level,
are difficult to obtain, particularly in medical imaging, where annotations
also require expert knowledge. Weakly-supervised learning can train models by
relying on weaker forms of annotation, such as scribbles. Here, we learn to
segment using scribble annotations in an adversarial game. With unpaired
segmentation masks, we train a multi-scale GAN to generate realistic
segmentation masks at multiple resolutions, while we use scribbles to learn
their correct position in the image. Central to the model's success is a novel
attention gating mechanism, which we condition with adversarial signals to act
as a shape prior, resulting in better object localization at multiple scales.
Subject to adversarial conditioning, the segmentor learns attention maps that
are semantic, suppress the noisy activations outside the objects, and reduce
the vanishing gradient problem in the deeper layers of the segmentor. We
evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical
(PPSS) datasets, and we report performance levels matching those achieved by
models trained with fully annotated segmentation masks. We also demonstrate
extensions in a variety of settings: semi-supervised learning; combining
multiple scribble sources (a crowdsourcing scenario) and multi-task learning
(combining scribble and mask supervision). We release expert-made scribble
annotations for the ACDC dataset, and the code used for the experiments, at
https://vios-s.github.io/multiscale-adversarial-attention-gatesComment: Paper accepted for publication at: IEEE Transaction on Medical
Imaging - Project page:
https://vios-s.github.io/multiscale-adversarial-attention-gate
Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging
Cluster of microcalcifications can be an early sign of breast cancer. In this
paper we propose a novel approach based on convolutional neural networks for
the detection and segmentation of microcalcification clusters. In this work we
used 283 mammograms to train and validate our model, obtaining an accuracy of
98.22% in the detection of preliminary suspect regions and of 97.47% in the
segmentation task. Our results show how deep learning could be an effective
tool to effectively support radiologists during mammograms examination.Comment: 13 pages, 7 figure
Sviluppo di un sistema di Deep Learning per segmentazione di immagini mammografiche
Il cancro al seno è una delle maggiori cause di mortalità tumorale fra le donne. La mammografia è l'esame di riferimento per lo screening di tumore alla mammella in donne con più di 40 anni: infatti, diverse meta-analisi hanno dimostrato una riduzione della mortalità per cancro al seno del 30%.
Le microcalcificazioni possono rappresentare un segnale precoce per la diagnosi di tumore alla mammella, rintracciabile in immagini mammografiche, ma sono spesso di difficile interpretazione per i radiologi a causa della sovrapposizione di lesioni maligne e benigne. Esse appaiono nella mammografia come regioni ad elevata intensità rispetto al background locale e hanno forme che vanno da geometrie circolari a geometrie fortemente irregolari, con contorni più o meno netti.
Breast Imaging Reporting and Dated System (BIRADS) ha standardizzato l'interpretazione delle microcalcificazioni: tipicamente benigne (BIRADS2), intermedie (BIRADS3), con elevata probabilità di essere maligne (BIRADS4), estremamente sospette di malignità (BIRADS5). La classificazione delle microcalcificazioni è basata sull'analisi della loro forma, densità e distribuzione all'interno della mammella.
Sfortunatamente le microcalcificazioni sono spesso difficili da rintracciare in quanto la mammella contiene diverse quantità di tessuto connettivo, ghiandolare e adiposo, organizzate in strutture sempre differenti. Ne risulta una gran varietà di pattern all'interno delle immagini.
La variabilità del tessuto mammario e la geometria di acquisizione proiettiva dell’immagine implicano l'impossibilità di utilizzare una semplice operazione di soglia basata sulla densità per il rintracciamento automatico delle calcificazioni. Il processo di detezione è ulteriormente complicato dalla grande variabilità della geometria delle microcalcificazioni, che inibisce una ricerca morfologica. Finora è stata proposta una gran varietà di algoritmi per il loro rintracciamento automatico, fra questi: metodi basati sulla trasformata wavelet, sistemi di filtraggio morfologico, analisi a multirisoluzione, reti bayesiane e SVM.
Date le difficoltà che mostrano i metodi classici in questo particolare problema, nel lavoro di tesi si propone un approccio basato su una rete neurale profonda di tipo convolutivo
Semi-supervised and weakly-supervised learning with spatio-temporal priors in medical image segmentation
Over the last decades, medical imaging techniques have
played a crucial role in healthcare, supporting radiologists
and facilitating patient diagnosis. With the advent of faster
and higher-quality imaging technologies, the amount of data
that is possible to collect for each patient is paving the way
toward personalised medicine. As a result, automating simple
image analysis operations, such as lesion localisation and
quantification, would greatly help clinicians focus energy
and attention on tasks best done by human intelligence.
Most recently, Artificial Intelligence (AI) research is accelerating
in healthcare, providing tools that often perform on par or
even better than humans in conceptually simple image processing
operations. In our work, we pay special attention to
the problem of automating semantic segmentation, where an
image is partitioned into multiple semantically meaningful
regions, separating the anatomical components of interest.
Unfortunately, developing effective AI segmentation tools usually
needs large quantities of annotated data. Conversely,
obtaining large-scale annotated datasets is difficult in medical
imaging, as it requires experts and is time-consuming.
For this reason, we develop automated methods to reduce the
need for collecting high-quality annotated data, both in terms
of the number and type of required annotations. We make
this possible by constraining the data representation learned
by our method to be semantic or by regularising the model
predictions to satisfy data-driven spatio-temporal priors. In
the thesis, we also open new avenues for future research using
AI with limited annotations, which we believe is key to
developing robust AI models for medical image analysis
Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts
Thanks to their ability to learn flexible data-driven losses, Generative
Adversarial Networks (GANs) are an integral part of many semi- and
weakly-supervised methods for medical image segmentation. GANs jointly optimise
a generator and an adversarial discriminator on a set of training data. After
training is complete, the discriminator is usually discarded, and only the
generator is used for inference. But should we discard discriminators? In this
work, we argue that training stable discriminators produces expressive loss
functions that we can re-use at inference to detect and \textit{correct}
segmentation mistakes. First, we identify key challenges and suggest possible
solutions to make discriminators re-usable at inference. Then, we show that we
can combine discriminators with image reconstruction costs (via decoders) to
endow a causal perspective to test-time training and further improve the model.
Our method is simple and improves the test-time performance of pre-trained
GANs. Moreover, we show that it is compatible with standard post-processing
techniques and it has the potential to be used for Online Continual Learning.
With our work, we open new research avenues for re-using adversarial
discriminators at inference. Our code is available at
https://vios-s.github.io/adversarial-test-time-training.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) https://www.melba-journal.org/papers/2022:014.htm