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- 1,645
- Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification635
A Multi-crop Convolutional Neural Network (MC-CNN) is presented to automatically extract nodule salient information by employing a novel multi-crop pooling strategy which crops different regions from convolutional feature maps and then applies max-pooling different times.
- Multi-oriented Text Detection with Fully Convolutional Networks596
A novel approach for text detection in natural images that consistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, I CDAR2015 and ICDAR2013.
- Few-Shot Image Recognition by Predicting Parameters from Activations594
A novel method that can adapt a pre-trained neural network to novel categories by directly predicting the parameters from the activations is proposed, which achieves the state-of-the-art classification accuracy on Novel categories by a significant margin while keeping comparable performance on the large-scale categories.
- 567
- Deep Co-Training for Semi-Supervised Image Recognition545
This paper presents Deep Co-Training, a deep learning based method inspired by the Co- Training framework, which outperforms the previous state-of-the-art methods by a large margin in semi-supervised image recognition.
- Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation414
It is revealed that the simple mechanism of copy-pasting bidirectionally between labeled and unlabeled data is good enough and the experiments show solid gains compared with other state-of-the-arts on various semi-supervised medical image segmentation datasets.
- PCL: Proposal Cluster Learning for Weakly Supervised Object Detection408
This paper first shows that instances can be assigned object or background labels directly based on proposal clusters for instance classifier refinement, and then shows that treating each cluster as a small new bag yields fewer ambiguities than the directly assigning label method.
- A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans317
This paper forms a fixed-point model which uses a predicted segmentation mask to shrink the input region and outperform the state-of-the-art by more than \(4\%\), measured by the average Dice-Sorensen Coefficient (DSC).
- Intriguing Findings of Frequency Selection for Image Deblurring295
This paper reveals an intriguing phenomenon that simply applying ReLU operation on the frequency domain of a blur image followed by inverse Fourier transform provides faithful information about the blur pattern, and builds a block that noticeably improves backbone architectures without introducing many parameters, while maintaining low computational complexity.
- 278
- Spatial–temporal convolutional neural networks for anomaly detection and localization in crowded scenes273
The proposed method for detecting and locating anomalous activities in video sequences of crowded scenes is a coupling of anomaly detection with a spatial-temporal Convolutional Neural Networks (CNN), which to the best of the knowledge has not been previously done.
- Learning Residual Images for Face Attribute Manipulation269
Instead of manipulating the whole image, this work proposes to learn the corresponding residual image defined as the difference between images before and after the manipulation, so that the manipulation can be operated efficiently with modest pixel modification.
- Abdominal multi-organ segmentation with organ-attention networks and statistical fusion255
This work introduces a novel framework for multi-organ segmentation of abdominal regions by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity.
- Single-Shot Object Detection with Enriched Semantics228
A novel single shot object detection network named Detection with Enriched Semantics (DES) is proposed, to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module.
- 201
- Deep Regression Forests for Age Estimation183
The proposed Deep Regression Forests (DRFs), an end-to-end model for age estimation, connect the split nodes to a fully connected layer of a convolutional neural network (CNN) and deal with inhomogeneous data by jointly learning input-dependant data partitions at the split node and data abstractions at the leaf nodes.
- Domain Adaptive Relational Reasoning for 3D Multi-organ Segmentation181
A novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models to medical data collected from different scanners and/or protocols (domains).
- Synthesize Then Compare: Detecting Failures and Anomalies for Semantic Segmentation178
This paper systematically study failure and anomaly detection for semantic segmentation and proposes a unified framework, consisting of two modules, to address these two related problems.
- A Survey on Label-Efficient Deep Image Segmentation: Bridging the Gap Between Weak Supervision and Dense Prediction159
A taxonomy is developed to organize the existing label-efficient image segmentation methods according to the supervision provided by different types of weak labels and discusses an important question: how to bridge the gap between weak supervision and dense prediction.
- Semi-Supervised 3D Abdominal Multi-Organ Segmentation Via Deep Multi-Planar Co-Training153
Deep Multi-Planar Co-Training is proposed, whose contributions can be divided into two folds: 1) the deep model is learned in a co-training style which can mine consensus information from multiple planes like the sagittal, coronal, and axial planes; 2) Multi-planar fusion is applied to generate more reliable pseudo-labels, which alleviates the errors occurring in the pseudo-Labels and thus can help to train better segmentation networks.
- A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation152
3D Convolutional Neural Networks are adopted to segment volumetric medical images and outperforms the 2D counterpart to a large margin since it can leverage the rich spatial information along all three axes.
- Weight Standardization148
Weight Standardization is proposed to accelerate deep network training by standardizing the weights in the convolutional layers, which is able to smooth the loss landscape by reducing the Lipschitz constants of the loss and the gradients.
- Deep Distance Transform for Tubular Structure Segmentation in CT Scans136
This work proposes a geometry-aware tubular structure segmentation method, Deep Distance Transform (DDT), which combines intuitions from the classical distance transform for skeletonization and modern deep segmentation networks, and applies it on six medical image datasets.
- 116
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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