354 research outputs found
Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation
Semi-supervised learning (SSL) has been proven beneficial for mitigating the
issue of limited labeled data especially on the task of volumetric medical
image segmentation. Unlike previous SSL methods which focus on exploring highly
confident pseudo-labels or developing consistency regularization schemes, our
empirical findings suggest that inconsistent decoder features emerge naturally
when two decoders strive to generate consistent predictions. Based on the
observation, we first analyze the treasure of discrepancy in learning towards
consistency, under both pseudo-labeling and consistency regularization
settings, and subsequently propose a novel SSL method called LeFeD, which
learns the feature-level discrepancy obtained from two decoders, by feeding the
discrepancy as a feedback signal to the encoder. The core design of LeFeD is to
enlarge the difference by training differentiated decoders, and then learn from
the inconsistent information iteratively. We evaluate LeFeD against eight
state-of-the-art (SOTA) methods on three public datasets. Experiments show
LeFeD surpasses competitors without any bells and whistles such as uncertainty
estimation and strong constraints, as well as setting a new state-of-the-art
for semi-supervised medical image segmentation. Code is available at
\textcolor{cyan}{https://github.com/maxwell0027/LeFeD
Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation
Annotation scarcity has become a major obstacle for training powerful
deep-learning models for medical image segmentation, restricting their
deployment in clinical scenarios. To address it, semi-supervised learning by
exploiting abundant unlabeled data is highly desirable to boost the model
training. However, most existing works still focus on limited medical tasks and
underestimate the potential of learning across diverse tasks and multiple
datasets. Therefore, in this paper, we introduce a \textbf{Ver}satile
\textbf{Semi}-supervised framework (VerSemi) to point out a new perspective
that integrates various tasks into a unified model with a broad label space, to
exploit more unlabeled data for semi-supervised medical image segmentation.
Specifically, we introduce a dynamic task-prompted design to segment various
targets from different datasets. Next, this unified model is used to identify
the foreground regions from all labeled data, to capture cross-dataset
semantics. Particularly, we create a synthetic task with a cutmix strategy to
augment foreground targets within the expanded label space. To effectively
utilize unlabeled data, we introduce a consistency constraint. This involves
aligning aggregated predictions from various tasks with those from the
synthetic task, further guiding the model in accurately segmenting foreground
regions during training. We evaluated our VerSemi model on four public
benchmarking datasets. Extensive experiments demonstrated that VerSemi can
consistently outperform the second-best method by a large margin (e.g., an
average 2.69\% Dice gain on four datasets), setting new SOTA performance for
semi-supervised medical image segmentation. The code will be released
Enhanced cancer therapy with cold-controlled drug release and photothermal warming enabled by one nanoplatform
Stimuli-responsive nanoparticles hold great promise for drug delivery to improve the safety and efficacy of cancer therapy. One of the most investigated stimuli-responsive strategies is to induce drug release by heating with laser, ultrasound, or electromagnetic field. More recently, cryosurgery (also called cryotherapy and cryoablation), destruction of diseased tissues by first cooling/freezing and then warming back, has been used to treat various diseases including cancer in the clinic. Here we developed a cold-responsive nanoparticle for controlled drug release as a result of the irreversible disassembly of the nanoparticle when cooled to below ∼10 °C. Furthermore, this nanoparticle can be used to generate localized heating under near infrared (NIR) laser irradiation, which can facilitate the warming process after cooling/freezing during cryosurgery. Indeed, the combination of this cold-responsive nanoparticle with ice cooling and NIR laser irradiation can greatly augment cancer destruction both in vitro and in vivo with no evident systemic toxicity
OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams
How to get insights from relational data streams in a timely manner is a hot
research topic. This type of data stream can present unique challenges, such as
distribution drifts, outliers, emerging classes, and changing features, which
have recently been described as open environment challenges for machine
learning. While existing studies have been done on incremental learning for
data streams, their evaluations are mostly conducted with manually partitioned
datasets. Thus, a natural question is how those open environment challenges
look like in real-world relational data streams and how existing incremental
learning algorithms perform on real datasets. To fill this gap, we develop an
Open Environment Benchmark named OEBench to evaluate open environment
challenges in relational data streams. Specifically, we investigate 55
real-world relational data streams and establish that open environment
scenarios are indeed widespread in real-world datasets, which presents
significant challenges for stream learning algorithms. Through benchmarks with
existing incremental learning algorithms, we find that increased data quantity
may not consistently enhance the model accuracy when applied in open
environment scenarios, where machine learning models can be significantly
compromised by missing values, distribution shifts, or anomalies in real-world
data streams. The current techniques are insufficient in effectively mitigating
these challenges posed by open environments. More researches are needed to
address real-world open environment challenges. All datasets and code are
open-sourced in https://github.com/sjtudyq/OEBench
Infusing Definiteness into Randomness: Rethinking Composition Styles for Deep Image Matting
We study the composition style in deep image matting, a notion that
characterizes a data generation flow on how to exploit limited foregrounds and
random backgrounds to form a training dataset. Prior art executes this flow in
a completely random manner by simply going through the foreground pool or by
optionally combining two foregrounds before foreground-background composition.
In this work, we first show that naive foreground combination can be
problematic and therefore derive an alternative formulation to reasonably
combine foregrounds. Our second contribution is an observation that matting
performance can benefit from a certain occurrence frequency of combined
foregrounds and their associated source foregrounds during training. Inspired
by this, we introduce a novel composition style that binds the source and
combined foregrounds in a definite triplet. In addition, we also find that
different orders of foreground combination lead to different foreground
patterns, which further inspires a quadruplet-based composition style. Results
under controlled experiments on four matting baselines show that our
composition styles outperform existing ones and invite consistent performance
improvement on both composited and real-world datasets. Code is available at:
https://github.com/coconuthust/composition_stylesComment: Accepted to AAAI 2023; 11 pages, 9 figures; Code is available at
https://github.com/coconuthust/composition_style
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
Self-supervised pre-training techniques have achieved remarkable progress in
Document AI. Most multimodal pre-trained models use a masked language modeling
objective to learn bidirectional representations on the text modality, but they
differ in pre-training objectives for the image modality. This discrepancy adds
difficulty to multimodal representation learning. In this paper, we propose
LayoutLMv3 to pre-train multimodal Transformers for Document AI with unified
text and image masking. Additionally, LayoutLMv3 is pre-trained with a
word-patch alignment objective to learn cross-modal alignment by predicting
whether the corresponding image patch of a text word is masked. The simple
unified architecture and training objectives make LayoutLMv3 a general-purpose
pre-trained model for both text-centric and image-centric Document AI tasks.
Experimental results show that LayoutLMv3 achieves state-of-the-art performance
not only in text-centric tasks, including form understanding, receipt
understanding, and document visual question answering, but also in
image-centric tasks such as document image classification and document layout
analysis. The code and models are publicly available at
https://aka.ms/layoutlmv3.Comment: Work in Progres
A Parallel Structured Divide-and-Conquer Algorithm for Symmetric Tridiagonal Eigenvalue Problems
© 2021 IEEE. Personal use of this material is permitted. PermissÃon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisÃng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] In this article, a parallel structured divide-and-conquer (PSDC) eigensolver is proposed for symmetric tridiagonal matrices based on ScaLAPACK and a parallel structured matrix multiplication algorithm, called PSMMA. Computing the eigenvectors via matrix-matrix multiplications is the most computationally expensive part of the divide-and-conquer algorithm, and one of the matrices involved in such multiplications is a rank-structured Cauchy-like matrix. By exploiting this particular property, PSMMA constructs the local matrices by using generators of Cauchy-like matrices without any communication, and further reduces the computation costs by using a structured low-rank approximation algorithm. Thus, both the communication and computation costs are reduced. Experimental results show that both PSMMA and PSDC are highly scalable and scale to 4096 processes at least. PSDC has better scalability than PHDC that was proposed in [16] and only scaled to 300 processes for the same matrices. Comparing with PDSTEDC in ScaLAPACK, PSDC is always faster and achieves 1.4x-1.6x speedup for some matrices with few deflations. PSDC is also comparable with ELPA, with PSDC being faster than ELPA when using few processes and a little slower when using many processes.The authors would like to thank the referees for their valuable comments which greatly improve the presentation of this article. This work was supported by National Natural Science Foundation of China (No. NNW2019ZT6-B20, NNW2019ZT6B21, NNW2019ZT5-A10, U1611261, 61872392, and U1811461), National Key RD Program of China (2018YFB0204303), NSF of Hunan (No. 2019JJ40339), NSF of NUDT (No. ZK18-03-01), Guangdong Natural Science Foundation (2018B030312002), and the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant 2016ZT06D211. The work of Jose E. Roman was supported by the Spanish Agencia Estatal de Investigacion (AEI) under project SLEPc-DA (PID2019-107379RB-I00).Liao, X.; Li, S.; Lu, Y.; Román Moltó, JE. (2021). A Parallel Structured Divide-and-Conquer Algorithm for Symmetric Tridiagonal Eigenvalue Problems. IEEE Transactions on Parallel and Distributed Systems. 32(2):367-378. https://doi.org/10.1109/TPDS.2020.3019471S36737832
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