27 research outputs found
Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction
Recent works have demonstrated that deep learning (DL) based compressed
sensing (CS) implementation can accelerate Magnetic Resonance (MR) Imaging by
reconstructing MR images from sub-sampled k-space data. However, network
architectures adopted in previous methods are all designed by handcraft. Neural
Architecture Search (NAS) algorithms can automatically build neural network
architectures which have outperformed human designed ones in several vision
tasks. Inspired by this, here we proposed a novel and efficient network for the
MR image reconstruction problem via NAS instead of manual attempts.
Particularly, a specific cell structure, which was integrated into the
model-driven MR reconstruction pipeline, was automatically searched from a
flexible pre-defined operation search space in a differentiable manner.
Experimental results show that our searched network can produce better
reconstruction results compared to previous state-of-the-art methods in terms
of PSNR and SSIM with 4-6 times fewer computation resources. Extensive
experiments were conducted to analyze how hyper-parameters affect
reconstruction performance and the searched structures. The generalizability of
the searched architecture was also evaluated on different organ MR datasets.
Our proposed method can reach a better trade-off between computation cost and
reconstruction performance for MR reconstruction problem with good
generalizability and offer insights to design neural networks for other medical
image applications. The evaluation code will be available at
https://github.com/yjump/NAS-for-CSMRI.Comment: To be appear in Computerized Medical Imaging and Graphic
Match4Rec: A Novel Recommendation Algorithm Based on Bidirectional Encoder Representation with the Matching Task
Characterizing users' interests accurately plays a significant role in an
effective recommender system. The sequential recommender system can learn
powerful hidden representations of users from successive user-item interactions
and dynamic users' preferences. To analyze such sequential data, conventional
methods mainly include Markov Chains (MCs) and Recurrent Neural Networks
(RNNs). Recently, the use of self-attention mechanisms and bi-directional
architectures have gained much attention. However, there still exists a major
limitation in previous works that they only model the user's main purposes in
the behavioral sequences separately and locally, and they lack the global
representation of the user's whole sequential behavior. To address this
limitation, we propose a novel bidirectional sequential recommendation
algorithm that integrates the user's local purposes with the global preference
by additive supervision of the matching task. We combine the mask task with the
matching task in the training process of the bidirectional encoder. A new
sample production method is also introduced to alleviate the effect of mask
noise. Our proposed model can not only learn bidirectional semantics from
users' behavioral sequences but also explicitly produces user representations
to capture user's global preference. Extensive empirical studies demonstrate
our approach considerably outperforms various state-of-the-art models.Comment: Accepted by ICONIP202
Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation
Recently, deep neural networks have greatly advanced histopathology image
segmentation but usually require abundant annotated data. However, due to the
gigapixel scale of whole slide images and pathologists' heavy daily workload,
obtaining pixel-level labels for supervised learning in clinical practice is
often infeasible. Alternatively, weakly-supervised segmentation methods have
been explored with less laborious image-level labels, but their performance is
unsatisfactory due to the lack of dense supervision. Inspired by the recent
success of self-supervised learning methods, we present a label-efficient
tissue prototype dictionary building pipeline and propose to use the obtained
prototypes to guide histopathology image segmentation. Particularly, taking
advantage of self-supervised contrastive learning, an encoder is trained to
project the unlabeled histopathology image patches into a discriminative
embedding space where these patches are clustered to identify the tissue
prototypes by efficient pathologists' visual examination. Then, the encoder is
used to map the images into the embedding space and generate pixel-level pseudo
tissue masks by querying the tissue prototype dictionary. Finally, the pseudo
masks are used to train a segmentation network with dense supervision for
better performance. Experiments on two public datasets demonstrate that our
human-machine interactive tissue prototype learning method can achieve
comparable segmentation performance as the fully-supervised baselines with less
annotation burden and outperform other weakly-supervised methods. Codes will be
available upon publication.Comment: IPMI2023 camera read
Uncertainty-driven Trajectory Truncation for Model-based Offline Reinforcement Learning
Equipped with the trained environmental dynamics, model-based offline
reinforcement learning (RL) algorithms can often successfully learn good
policies from fixed-sized datasets, even some datasets with poor quality.
Unfortunately, however, it can not be guaranteed that the generated samples
from the trained dynamics model are reliable (e.g., some synthetic samples may
lie outside of the support region of the static dataset). To address this
issue, we propose Trajectory Truncation with Uncertainty (TATU), which
adaptively truncates the synthetic trajectory if the accumulated uncertainty
along the trajectory is too large. We theoretically show the performance bound
of TATU to justify its benefits. To empirically show the advantages of TATU, we
first combine it with two classical model-based offline RL algorithms, MOPO and
COMBO. Furthermore, we integrate TATU with several off-the-shelf model-free
offline RL algorithms, e.g., BCQ. Experimental results on the D4RL benchmark
show that TATU significantly improves their performance, often by a large
margin
Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification
Deep learning-based melanoma classification with dermoscopic images has
recently shown great potential in automatic early-stage melanoma diagnosis.
However, limited by the significant data imbalance and obvious extraneous
artifacts, i.e., the hair and ruler markings, discriminative feature extraction
from dermoscopic images is very challenging. In this study, we seek to resolve
these problems respectively towards better representation learning for lesion
features. Specifically, a GAN-based data augmentation (GDA) strategy is adapted
to generate synthetic melanoma-positive images, in conjunction with the
proposed implicit hair denoising (IHD) strategy. Wherein the hair-related
representations are implicitly disentangled via an auxiliary classifier network
and reversely sent to the melanoma-feature extraction backbone for better
melanoma-specific representation learning. Furthermore, to train the IHD
module, the hair noises are additionally labeled on the ISIC2020 dataset,
making it the first large-scale dermoscopic dataset with annotation of
hair-like artifacts. Extensive experiments demonstrate the superiority of the
proposed framework as well as the effectiveness of each component. The improved
dataset publicly avaliable at https://github.com/kirtsy/DermoscopicDataset.Comment: ICONIP 2021 conferenc
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures