98 research outputs found
DEEP LEARNING BASED METHODS FOR ULTRASOUND IMAGE SEGMENTATION AND MAGNETIC RESONANCE IMAGE RECONSTRUCTION
In this thesis, we develop various deep learning (DL) based approaches to address two medical image analysis problems. In the first problem, we focus on computer assisted orthopedic surgery (CAOS) applications that use ultrasound as intra-operative imaging modality. This problem requires an automatic and real-time algorithm to detect and segment bone surfaces and shadows in order to provide guidance for the orthopedic surgeon to a standardized diagnostic viewing plane with minimal artifacts. Due to the limitation of relatively small datasets and image differences from multiple ultrasound machines, we develop DL-based frameworks that leverage a local phase filtering technique and integrate it into the DL framework, thus improving the robustness.
Finally, we propose a fast and accurate Magnetic Resonance Imaging (MRI) image reconstruction framework using a novel Convolutional Recurrent Neural Network (CRNN). Extensive experiments and evaluation on knee and brain datasets have shown its outstanding results compared to the traditional compressed sensing and other DL-based methods. Furthermore, we extend this method to enable multi sequence-reconstruction where T2-weighted MRI image can provide guidance and improvement to the reconstruction of amid proton transfer-weighted MRI image
Open World Classification with Adaptive Negative Samples
Open world classification is a task in natural language processing with key
practical relevance and impact. Since the open or {\em unknown} category data
only manifests in the inference phase, finding a model with a suitable decision
boundary accommodating for the identification of known classes and
discrimination of the open category is challenging. The performance of existing
models is limited by the lack of effective open category data during the
training stage or the lack of a good mechanism to learn appropriate decision
boundaries. We propose an approach based on \underline{a}daptive
\underline{n}egative \underline{s}amples (ANS) designed to generate effective
synthetic open category samples in the training stage and without requiring any
prior knowledge or external datasets. Empirically, we find a significant
advantage in using auxiliary one-versus-rest binary classifiers, which
effectively utilize the generated negative samples and avoid the complex
threshold-seeking stage in previous works. Extensive experiments on three
benchmark datasets show that ANS achieves significant improvements over
state-of-the-art methods.Comment: Accepted by EMNLP 2021 (Main Track, Long Paper
LViT: Language meets Vision Transformer in Medical Image Segmentation
Deep learning has been widely used in medical image segmentation and other
aspects. However, the performance of existing medical image segmentation models
has been limited by the challenge of obtaining sufficient high-quality labeled
data due to the prohibitive data annotation cost. To alleviate this limitation,
we propose a new text-augmented medical image segmentation model LViT (Language
meets Vision Transformer). In our LViT model, medical text annotation is
incorporated to compensate for the quality deficiency in image data. In
addition, the text information can guide to generate pseudo labels of improved
quality in the semi-supervised learning. We also propose an Exponential Pseudo
label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM)
preserve local image features in semi-supervised LViT setting. In our model, LV
(Language-Vision) loss is designed to supervise the training of unlabeled
images using text information directly. For evaluation, we construct three
multimodal medical segmentation datasets (image + text) containing X-rays and
CT images. Experimental results show that our proposed LViT has superior
segmentation performance in both fully-supervised and semi-supervised setting.
The code and datasets are available at https://github.com/HUANGLIZI/LViT.Comment: Accepted by IEEE Transactions on Medical Imaging (TMI
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