248 research outputs found
Real-Time Gate Reassignment Based on Flight Delay Feature in Hub Airport
Appropriate gate reassignment is crucially important in efficiency improvement on airport sources and service quality of travelers. The paper divides delay flight into certain delay time flight and uncertain delay time flight based on flight delay feature. The main objective functions of model are to minimize the disturbance led by gate reassignment in the case of certain delay time flight and uncertain delay time flight, respectively. Another objective function of model is to build penalty function when the gate reassignment of certain delay time flight influences uncertain delay time flight. Ant colony algorithm (ACO) is presented to simulate and verify the effectiveness of the model. The comparison between simulation result and artificial assignment shows that the result coming from ACO is obvious prior to the result coming from artificial assignment. The maximum disturbance of gate assignment is decreased by 13.64%, and the operation time of ACO is 118 s. The results show that the strategy of gate reassignment is feasible and effective
Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification
Computer-aided pathology diagnosis based on the classification of Whole Slide
Image (WSI) plays an important role in clinical practice, and it is often
formulated as a weakly-supervised Multiple Instance Learning (MIL) problem.
Existing methods solve this problem from either a bag classification or an
instance classification perspective. In this paper, we propose an end-to-end
weakly supervised knowledge distillation framework (WENO) for WSI
classification, which integrates a bag classifier and an instance classifier in
a knowledge distillation framework to mutually improve the performance of both
classifiers. Specifically, an attention-based bag classifier is used as the
teacher network, which is trained with weak bag labels, and an instance
classifier is used as the student network, which is trained using the
normalized attention scores obtained from the teacher network as soft pseudo
labels for the instances in positive bags. An instance feature extractor is
shared between the teacher and the student to further enhance the knowledge
exchange between them. In addition, we propose a hard positive instance mining
strategy based on the output of the student network to force the teacher
network to keep mining hard positive instances. WENO is a plug-and-play
framework that can be easily applied to any existing attention-based bag
classification methods. Extensive experiments on five datasets demonstrate the
efficiency of WENO. Code is available at https://github.com/miccaiif/WENO.Comment: Accepted by NeurIPS 202
Unsupervised Low Light Image Enhancement Using SNR-Aware Swin Transformer
Image captured under low-light conditions presents unpleasing artifacts,
which debilitate the performance of feature extraction for many upstream visual
tasks. Low-light image enhancement aims at improving brightness and contrast,
and further reducing noise that corrupts the visual quality. Recently, many
image restoration methods based on Swin Transformer have been proposed and
achieve impressive performance. However, On one hand, trivially employing Swin
Transformer for low-light image enhancement would expose some artifacts,
including over-exposure, brightness imbalance and noise corruption, etc. On the
other hand, it is impractical to capture image pairs of low-light images and
corresponding ground-truth, i.e. well-exposed image in same visual scene. In
this paper, we propose a dual-branch network based on Swin Transformer, guided
by a signal-to-noise ratio prior map which provides the spatial-varying
information for low-light image enhancement. Moreover, we leverage unsupervised
learning to construct the optimization objective based on Retinex model, to
guide the training of proposed network. Experimental results demonstrate that
the proposed model is competitive with the baseline models
The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification
This paper introduces the novel concept of few-shot weakly supervised
learning for pathology Whole Slide Image (WSI) classification, denoted as FSWC.
A solution is proposed based on prompt learning and the utilization of a large
language model, GPT-4. Since a WSI is too large and needs to be divided into
patches for processing, WSI classification is commonly approached as a Multiple
Instance Learning (MIL) problem. In this context, each WSI is considered a bag,
and the obtained patches are treated as instances. The objective of FSWC is to
classify both bags and instances with only a limited number of labeled bags.
Unlike conventional few-shot learning problems, FSWC poses additional
challenges due to its weak bag labels within the MIL framework. Drawing
inspiration from the recent achievements of vision-language models (V-L models)
in downstream few-shot classification tasks, we propose a two-level prompt
learning MIL framework tailored for pathology, incorporating language prior
knowledge. Specifically, we leverage CLIP to extract instance features for each
patch, and introduce a prompt-guided pooling strategy to aggregate these
instance features into a bag feature. Subsequently, we employ a small number of
labeled bags to facilitate few-shot prompt learning based on the bag features.
Our approach incorporates the utilization of GPT-4 in a question-and-answer
mode to obtain language prior knowledge at both the instance and bag levels,
which are then integrated into the instance and bag level language prompts.
Additionally, a learnable component of the language prompts is trained using
the available few-shot labeled data. We conduct extensive experiments on three
real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer,
demonstrating the notable performance of the proposed method in bag and
instance classification. All codes will be made publicly accessible
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