333,199 research outputs found
Unsupervised Lesion Detection via Image Restoration with a Normative Prior
Unsupervised lesion detection is a challenging problem that requires
accurately estimating normative distributions of healthy anatomy and detecting
lesions as outliers without training examples. Recently, this problem has
received increased attention from the research community following the advances
in unsupervised learning with deep learning. Such advances allow the estimation
of high-dimensional distributions, such as normative distributions, with higher
accuracy than previous methods.The main approach of the recently proposed
methods is to learn a latent-variable model parameterized with networks to
approximate the normative distribution using example images showing healthy
anatomy, perform prior-projection, i.e. reconstruct the image with lesions
using the latent-variable model, and determine lesions based on the differences
between the reconstructed and original images. While being promising, the
prior-projection step often leads to a large number of false positives. In this
work, we approach unsupervised lesion detection as an image restoration problem
and propose a probabilistic model that uses a network-based prior as the
normative distribution and detect lesions pixel-wise using MAP estimation. The
probabilistic model punishes large deviations between restored and original
images, reducing false positives in pixel-wise detections. Experiments with
gliomas and stroke lesions in brain MRI using publicly available datasets show
that the proposed approach outperforms the state-of-the-art unsupervised
methods by a substantial margin, +0.13 (AUC), for both glioma and stroke
detection. Extensive model analysis confirms the effectiveness of MAP-based
image restoration.Comment: Extended version of 'Unsupervised Lesion Detection via Image
Restoration with a Normative Prior' (MIDL2019
Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia
In this work, we have concentrated our efforts on the interpretability of
classification results coming from a fully convolutional neural network.
Motivated by the classification of oesophageal tissue for real-time detection
of early squamous neoplasia, the most frequent kind of oesophageal cancer in
Asia, we present a new dataset and a novel deep learning method that by means
of deep supervision and a newly introduced concept, the embedded Class
Activation Map (eCAM), focuses on the interpretability of results as a design
constraint of a convolutional network. We present a new approach to visualise
attention that aims to give some insights on those areas of the oesophageal
tissue that lead a network to conclude that the images belong to a particular
class and compare them with those visual features employed by clinicians to
produce a clinical diagnosis. In comparison to a baseline method which does not
feature deep supervision but provides attention by grafting Class Activation
Maps, we improve the F1-score from 87.3% to 92.7% and provide more detailed
attention maps
NFormer: Robust Person Re-identification with Neighbor Transformer
Person re-identification aims to retrieve persons in highly varying settings
across different cameras and scenarios, in which robust and discriminative
representation learning is crucial. Most research considers learning
representations from single images, ignoring any potential interactions between
them. However, due to the high intra-identity variations, ignoring such
interactions typically leads to outlier features. To tackle this issue, we
propose a Neighbor Transformer Network, or NFormer, which explicitly models
interactions across all input images, thus suppressing outlier features and
leading to more robust representations overall. As modelling interactions
between enormous amount of images is a massive task with lots of distractors,
NFormer introduces two novel modules, the Landmark Agent Attention, and the
Reciprocal Neighbor Softmax. Specifically, the Landmark Agent Attention
efficiently models the relation map between images by a low-rank factorization
with a few landmarks in feature space. Moreover, the Reciprocal Neighbor
Softmax achieves sparse attention to relevant -- rather than all -- neighbors
only, which alleviates interference of irrelevant representations and further
relieves the computational burden. In experiments on four large-scale datasets,
NFormer achieves a new state-of-the-art. The code is released at
\url{https://github.com/haochenheheda/NFormer}.Comment: 8 pages, 7 figures, CVPR2022 poste
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