1,134 research outputs found
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Large, labeled datasets have driven deep learning methods to achieve
expert-level performance on a variety of medical imaging tasks. We present
CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240
patients. We design a labeler to automatically detect the presence of 14
observations in radiology reports, capturing uncertainties inherent in
radiograph interpretation. We investigate different approaches to using the
uncertainty labels for training convolutional neural networks that output the
probability of these observations given the available frontal and lateral
radiographs. On a validation set of 200 chest radiographic studies which were
manually annotated by 3 board-certified radiologists, we find that different
uncertainty approaches are useful for different pathologies. We then evaluate
our best model on a test set composed of 500 chest radiographic studies
annotated by a consensus of 5 board-certified radiologists, and compare the
performance of our model to that of 3 additional radiologists in the detection
of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the
model ROC and PR curves lie above all 3 radiologist operating points. We
release the dataset to the public as a standard benchmark to evaluate
performance of chest radiograph interpretation models.
The dataset is freely available at
https://stanfordmlgroup.github.io/competitions/chexpert .Comment: Published in AAAI 201
Conditional Consistency Regularization for Semi-supervised Multi-label Classification
In practical scenarios, a sample may have multiple labels that reveal its classes instead of a single label, which is widely known as multi-label classification (MLC). However, some practical situations may lack reliable labels due to the high cost, time-consuming and professional labelling process. Although Semi-supervised classification may become a potential solution, most of the outstanding existing methods are customized for the single-label situation and ignore multi-label situations. Consistency regularization has performed great success in Weakly/Semi-supervised Single-label classification (SS-SLC), but few efforts have been devoted to semi-supervised Multi-label classification (SS-MLC). A simple solution for introducing consistency regularization to SS-MLC is to regularize predictions of models to be consistent with different augmentation of the same image. Nonetheless, the solution lacks attention to label relations which are crucial components of Multi-label classification.
In the thesis, I go beyond the consistency regularization in SS-SLC and propose Conditional Consistency Regularization (CCR) that is designed for SS-MLC. To be specific, we make potential labels (grand-truth label for labeled samples, pseudo-label for unlabeled samples) conditioned on different label states (i.e., positive, negative, or unknown for each class). By regularizing the two predictions to be invariant, the model can learn label relations implicitly between two different label states, which can boost classification performance. The comprehensive experiments that are conducted on different datasets show that the proposed method can surpass state-of-art SS-MLC and MLC methods by a large gap
The Emerging Trends of Multi-Label Learning
Exabytes of data are generated daily by humans, leading to the growing need
for new efforts in dealing with the grand challenges for multi-label learning
brought by big data. For example, extreme multi-label classification is an
active and rapidly growing research area that deals with classification tasks
with an extremely large number of classes or labels; utilizing massive data
with limited supervision to build a multi-label classification model becomes
valuable for practical applications, etc. Besides these, there are tremendous
efforts on how to harvest the strong learning capability of deep learning to
better capture the label dependencies in multi-label learning, which is the key
for deep learning to address real-world classification tasks. However, it is
noted that there has been a lack of systemic studies that focus explicitly on
analyzing the emerging trends and new challenges of multi-label learning in the
era of big data. It is imperative to call for a comprehensive survey to fulfill
this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202
Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects
Object detection is an essential and fundamental task in computer vision and
satellite image processing. Existing deep learning methods have achieved
impressive performance thanks to the availability of large-scale annotated
datasets. Yet, in real-world applications the availability of labels is
limited. In this context, few-shot object detection (FSOD) has emerged as a
promising direction, which aims at enabling the model to detect novel objects
with only few of them annotated. However, many existing FSOD algorithms
overlook a critical issue: when an input image contains multiple novel objects
and only a subset of them are annotated, the unlabeled objects will be
considered as background during training. This can cause confusions and
severely impact the model's ability to recall novel objects. To address this
issue, we propose a self-training-based FSOD (ST-FSOD) approach, which
incorporates the self-training mechanism into the few-shot fine-tuning process.
ST-FSOD aims to enable the discovery of novel objects that are not annotated,
and take them into account during training. On the one hand, we devise a
two-branch region proposal networks (RPN) to separate the proposal extraction
of base and novel objects, On another hand, we incorporate the student-teacher
mechanism into RPN and the region of interest (RoI) head to include those
highly confident yet unlabeled targets as pseudo labels. Experimental results
demonstrate that our proposed method outperforms the state-of-the-art in
various FSOD settings by a large margin. The codes will be publicly available
at https://github.com/zhu-xlab/ST-FSOD
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