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Register a new userSegmenting Microcalcifications in Mammograms and its Applications
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Added by
Shai Bagon
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Microcalcifications are small deposits of calcium that appear in mammograms as bright white specks on the soft tissue background of the breast. Microcalcifications may be a unique indication for Ductal Carcinoma in Situ breast cancer, and therefore their accurate detection is crucial for diagnosis and screening. Manual detection of these tiny calcium residues in mammograms is both time-consuming and error-prone, even for expert radiologists, since these microcalcifications are small and can be easily missed. Existing computerized algorithms for detecting and segmenting microcalcifications tend to suffer from a high false-positive rate, hindering their widespread use. In this paper, we propose an accurate calcification segmentation method using deep learning. We specifically address the challenge of keeping the false positive rate low by suggesting a strategy for focusing the hard pixels in the training phase. Furthermore, our accurate segmentation enables extracting meaningful statistics on clusters of microcalcifications.
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In this paper, we tackle video panoptic segmentation, a task that requires assigning semantic classes and track identities to all pixels in a video. To study this important problem in a setting that requires a continuous interpretation of sensory data, we present a new benchmark: Segmenting and Tracking Every Pixel (STEP), encompassing two datasets, KITTI-STEP, and MOTChallenge-STEP together with a new evaluation metric. Our work is the first that targets this task in a real-world setting that requires dense interpretation in both spatial and temporal domains. As the ground-truth for this task is difficult and expensive to obtain, existing datasets are either constructed synthetically or only sparsely annotated within short video clips. By contrast, our datasets contain long video sequences, providing challenging examples and a test-bed for studying long-term pixel-precise segmentation and tracking. For measuring the performance, we propose a novel evaluation metric Segmentation and Tracking Quality (STQ) that fairly balances semantic and tracking aspects of this task and is suitable for evaluating sequences of arbitrary length. We will make our datasets, metric, and baselines publicly available.
Low-rank modeling generally refers to a class of methods that solve problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing and bioinformatics. Recently, much progress has been made in theories, algorithms and applications of low-rank modeling, such as exact low-rank matrix recovery via convex programming and matrix completion applied to collaborative filtering. These advances have brought more and more attentions to this topic. In this paper, we review the recent advance of low-rank modeling, the state-of-the-art algorithms, and related applications in image analysis. We first give an overview to the concept of low-rank modeling and challenging problems in this area. Then, we summarize the models and algorithms for low-rank matrix recovery and illustrate their advantages and limitations with numerical experiments. Next, we introduce a few applications of low-rank modeling in the context of image analysis. Finally, we conclude this paper with some discussions.
Detecting and segmenting individual objects, regardless of their category, is crucial for many applications such as action detection or robotic interaction. While this problem has been well-studied under the classic formulation of spatio-temporal grouping, state-of-the-art approaches do not make use of learning-based methods. To bridge this gap, we propose a simple learning-based approach for spatio-temporal grouping. Our approach leverages motion cues from optical flow as a bottom-up signal for separating objects from each other. Motion cues are then combined with appearance cues that provide a generic objectness prior for capturing the full extent of objects. We show that our approach outperforms all prior work on the benchmark FBMS dataset. One potential worry with learning-based methods is that they might overfit to the particular type of objects that they have been trained on. To address this concern, we propose two new benchmarks for generic, moving object detection, and show that our model matches top-down methods on common categories, while significantly out-performing both top-down and bottom-up methods on never-before-seen categories.
Most recent transformer-based models show impressive performance on vision tasks, even better than Convolution Neural Networks (CNN). In this work, we present a novel, flexible, and effective transformer-based model for high-quality instance segmentation. The proposed method, Segmenting Objects with TRansformers (SOTR), simplifies the segmentation pipeline, building on an alternative CNN backbone appended with two parallel subtasks: (1) predicting per-instance category via transformer and (2) dynamically generating segmentation mask with the multi-level upsampling module. SOTR can effectively extract lower-level feature representations and capture long-range context dependencies by Feature Pyramid Network (FPN) and twin transformer, respectively. Meanwhile, compared with the original transformer, the proposed twin transformer is time- and resource-efficient since only a row and a column attention are involved to encode pixels. Moreover, SOTR is easy to be incorporated with various CNN backbones and transformer model variants to make considerable improvements for the segmentation accuracy and training convergence. Extensive experiments show that our SOTR performs well on the MS COCO dataset and surpasses state-of-the-art instance segmentation approaches. We hope our simple but strong framework could serve as a preferment baseline for instance-level recognition. Our code is available at https://github.com/easton-cau/SOTR.
Fisher vector has been widely used in many multimedia retrieval and visual recognition applications with good performance. However, the computation complexity prevents its usage in real-time video monitoring. In this work, we proposed and implemented GPU-FV, a fast Fisher vector extraction method with the help of modern GPUs. The challenge of implementing Fisher vector on GPUs lies in the data dependency in feature extraction and expensive memory access in Fisher vector computing. To handle these challenges, we carefully designed GPU-FV in a way that utilizes the computing power of GPU as much as possible, and applied optimizations such as loop tiling to boost the performance. GPU-FV is about 12 times faster than the CPU version, and 50% faster than a non-optimized GPU implementation. For standard video input (320*240), GPU-FV can process each frame within 34ms on a model GPU. Our experiments show that GPU-FV obtains a similar recognition accuracy as traditional FV on VOC 2007 and Caltech 256 image sets. We also applied GPU-FV for realtime video monitoring tasks and found that GPU-FV outperforms a number of previous works. Especially, when the number of training examples are small, GPU-FV outperforms the recent popular deep CNN features borrowed from ImageNet. The code can be downloaded from the following link https://bitbucket.org/mawenjing/gpu-fv.
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