151 research outputs found
Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network
Skin lesion is a severe disease in world-wide extent. Early detection of
melanoma in dermoscopy images significantly increases the survival rate.
However, the accurate recognition of melanoma is extremely challenging due to
the following reasons, e.g. low contrast between lesions and skin, visual
similarity between melanoma and non-melanoma lesions, etc. Hence, reliable
automatic detection of skin tumors is very useful to increase the accuracy and
efficiency of pathologists. International Skin Imaging Collaboration (ISIC) is
a challenge focusing on the automatic analysis of skin lesion. In this paper,
we proposed two deep learning methods to address all the three tasks announced
in ISIC 2017, i.e. lesion segmentation (task 1), lesion dermoscopic feature
extraction (task 2) and lesion classification (task 3). A deep learning
framework consisting of two fully-convolutional residual networks (FCRN) is
proposed to simultaneously produce the segmentation result and the coarse
classification result. A lesion index calculation unit (LICU) is developed to
refine the coarse classification results by calculating the distance heat-map.
A straight-forward CNN is proposed for the dermoscopic feature extraction task.
To our best knowledges, we are not aware of any previous work proposed for this
task. The proposed deep learning frameworks were evaluated on the ISIC 2017
testing set. Experimental results show the promising accuracies of our
frameworks, i.e. 0.718 for task 1, 0.833 for task 2 and 0.823 for task 3 were
achieved.Comment: ISIC201
An investigation of automatic processing techniques for time-lapse microscope images
The analysis of time-lapse microscope images is a recent popular research topic. Processing techniques have been employed in such studies to extract important information about cells—e.g., cell number or alterations of cellular features—for various tasks. However, few studies provide acceptable results in practical applications because they cannot simultaneously solve the core challenges that are shared by most cell datasets: the image contrast is extremely low; the distribution of grey scale is non-uniform; images are noisy; the number of cells is large, etc. These factors also make manual processing an extremely laborious task.
To improve the efficiency of related biological analyses and disease diagnoses. This thesis establishes a framework in these directions: a new segmentation method for cell images is designed as the foundation of an automatic approach for the measurement of cellular features. The newly proposed segmentation method achieves substantial improvements in the detection of cell filopodia. An automatic measuring mechanism for cell features is established in the designed framework. The measuring component enables the system to provide quantitative information about various cell features that are useful in biological research. A novel cell-tracking framework is constructed to monitor the alterations of cells with an accuracy of cell tracking above 90%.
To address the issue of processing speed, two fast-processing techniques have been developed to complete edge detection and visual tracking. For edge detection, the new detector is a hybrid approach that is based on the Canny operator and fuzzy entropy theory. The method calculates the fuzzy entropy of gradients from an image to decide the threshold for the Canny operator. For visual tracking, a newly defined feature is employed in the fast-tracking mechanism to recognize different cell events with tracking accuracy: i.e., 97.66%, and processing speed, i.e., 0.578s/frame
Understanding the Complexity Gains of Single-Task RL with a Curriculum
Reinforcement learning (RL) problems can be challenging without well-shaped
rewards. Prior work on provably efficient RL methods generally proposes to
address this issue with dedicated exploration strategies. However, another way
to tackle this challenge is to reformulate it as a multi-task RL problem, where
the task space contains not only the challenging task of interest but also
easier tasks that implicitly function as a curriculum. Such a reformulation
opens up the possibility of running existing multi-task RL methods as a more
efficient alternative to solving a single challenging task from scratch. In
this work, we provide a theoretical framework that reformulates a single-task
RL problem as a multi-task RL problem defined by a curriculum. Under mild
regularity conditions on the curriculum, we show that sequentially solving each
task in the multi-task RL problem is more computationally efficient than
solving the original single-task problem, without any explicit exploration
bonuses or other exploration strategies. We also show that our theoretical
insights can be translated into an effective practical learning algorithm that
can accelerate curriculum learning on simulated robotic tasks
Generative Adversarial Networks for Video-to-Video Domain Adaptation
Endoscopic videos from multicentres often have different imaging conditions,
e.g., color and illumination, which make the models trained on one domain
usually fail to generalize well to another. Domain adaptation is one of the
potential solutions to address the problem. However, few of existing works
focused on the translation of video-based data. In this work, we propose a
novel generative adversarial network (GAN), namely VideoGAN, to transfer the
video-based data across different domains. As the frames of a video may have
similar content and imaging conditions, the proposed VideoGAN has an X-shape
generator to preserve the intra-video consistency during translation.
Furthermore, a loss function, namely color histogram loss, is proposed to tune
the color distribution of each translated frame. Two colonoscopic datasets from
different centres, i.e., CVC-Clinic and ETIS-Larib, are adopted to evaluate the
performance of domain adaptation of our VideoGAN. Experimental results
demonstrate that the adapted colonoscopic video generated by our VideoGAN can
significantly boost the segmentation accuracy, i.e., an improvement of 5%, of
colorectal polyps on multicentre datasets. As our VideoGAN is a general network
architecture, we also evaluate its performance with the CamVid driving video
dataset on the cloudy-to-sunny translation task. Comprehensive experiments show
that the domain gap could be substantially narrowed down by our VideoGAN.Comment: Accepted by AAAI 202
Tunable Soft Prompts are Messengers in Federated Learning
Federated learning (FL) enables multiple participants to collaboratively
train machine learning models using decentralized data sources, alleviating
privacy concerns that arise from directly sharing local data. However, the lack
of model privacy protection in FL becomes an unneglectable challenge,
especially when people want to federally finetune models based on a proprietary
large language model. In this study, we propose a novel FL training approach
that accomplishes information exchange among participants via tunable soft
prompts. These soft prompts, updated and transmitted between the server and
clients, assume the role of the global model parameters and serve as messengers
to deliver useful knowledge from the local data and global model. As the global
model itself is not required to be shared and the local training is conducted
based on an auxiliary model with fewer parameters than the global model, the
proposed approach provides protection for the global model while reducing
communication and computation costs in FL. Extensive experiments show the
effectiveness of the proposed approach compared to several baselines. We have
released the source code at
\url{https://github.com/alibaba/FederatedScope/tree/fedsp/federatedscope/nlp/fedsp}.Comment: Accepted by EMNLP-2
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