196,186 research outputs found
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
Multi-label image classification is a fundamental but challenging task
towards general visual understanding. Existing methods found the region-level
cues (e.g., features from RoIs) can facilitate multi-label classification.
Nevertheless, such methods usually require laborious object-level annotations
(i.e., object labels and bounding boxes) for effective learning of the
object-level visual features. In this paper, we propose a novel and efficient
deep framework to boost multi-label classification by distilling knowledge from
weakly-supervised detection task without bounding box annotations.
Specifically, given the image-level annotations, (1) we first develop a
weakly-supervised detection (WSD) model, and then (2) construct an end-to-end
multi-label image classification framework augmented by a knowledge
distillation module that guides the classification model by the WSD model
according to the class-level predictions for the whole image and the
object-level visual features for object RoIs. The WSD model is the teacher
model and the classification model is the student model. After this cross-task
knowledge distillation, the performance of the classification model is
significantly improved and the efficiency is maintained since the WSD model can
be safely discarded in the test phase. Extensive experiments on two large-scale
datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior
performances over the state-of-the-art methods on both performance and
efficiency.Comment: accepted by ACM Multimedia 2018, 9 pages, 4 figures, 5 table
HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery
The wide range of research in deep learning-based medical image segmentation
pushed the boundaries in a multitude of applications. A clinically relevant
problem that received less attention is the handling of scans with irregular
anatomy, e.g., after organ resection. State-of-the-art segmentation models
often lead to organ hallucinations, i.e., false-positive predictions of organs,
which cannot be alleviated by oversampling or post-processing. Motivated by the
increasing need to develop robust deep learning models, we propose HALOS for
abdominal organ segmentation in MR images that handles cases after organ
resection surgery. To this end, we combine missing organ classification and
multi-organ segmentation tasks into a multi-task model, yielding a
classification-assisted segmentation pipeline. The segmentation network learns
to incorporate knowledge about organ existence via feature fusion modules.
Extensive experiments on a small labeled test set and large-scale UK Biobank
data demonstrate the effectiveness of our approach in terms of higher
segmentation Dice scores and near-to-zero false positive prediction rate.Comment: To be published in proceedings of Information Processing In Medical
Imaging (IPMI) 202
DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection
Contour detection has been a fundamental component in many image segmentation
and object detection systems. Most previous work utilizes low-level features
such as texture or saliency to detect contours and then use them as cues for a
higher-level task such as object detection. However, we claim that recognizing
objects and predicting contours are two mutually related tasks. Contrary to
traditional approaches, we show that we can invert the commonly established
pipeline: instead of detecting contours with low-level cues for a higher-level
recognition task, we exploit object-related features as high-level cues for
contour detection.
We achieve this goal by means of a multi-scale deep network that consists of
five convolutional layers and a bifurcated fully-connected sub-network. The
section from the input layer to the fifth convolutional layer is fixed and
directly lifted from a pre-trained network optimized over a large-scale object
classification task. This section of the network is applied to four different
scales of the image input. These four parallel and identical streams are then
attached to a bifurcated sub-network consisting of two independently-trained
branches. One branch learns to predict the contour likelihood (with a
classification objective) whereas the other branch is trained to learn the
fraction of human labelers agreeing about the contour presence at a given point
(with a regression criterion).
We show that without any feature engineering our multi-scale deep learning
approach achieves state-of-the-art results in contour detection.Comment: Accepted to CVPR 201
One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification
The recent surge in performance for image analysis of digitised pathology
slides can largely be attributed to the advance of deep learning. Deep models
can be used to initially localise various structures in the tissue and hence
facilitate the extraction of interpretable features for biomarker discovery.
However, these models are typically trained for a single task and therefore
scale poorly as we wish to adapt the model for an increasing number of
different tasks. Also, supervised deep learning models are very data hungry and
therefore rely on large amounts of training data to perform well. In this paper
we present a multi-task learning approach for segmentation and classification
of nuclei, glands, lumen and different tissue regions that leverages data from
multiple independent data sources. While ensuring that our tasks are aligned by
the same tissue type and resolution, we enable simultaneous prediction with a
single network. As a result of feature sharing, we also show that the learned
representation can be used to improve downstream tasks, including nuclear
classification and signet ring cell detection. As part of this work, we use a
large dataset consisting of over 600K objects for segmentation and 440K patches
for classification and make the data publicly available. We use our approach to
process the colorectal subset of TCGA, consisting of 599 whole-slide images, to
localise 377 million, 900K and 2.1 million nuclei, glands and lumen
respectively. We make this resource available to remove a major barrier in the
development of explainable models for computational pathology
Classification of malignant and benign lung nodule and prediction of image label class using multi-deep model
Lung cancer has been listed as one of the world’s leading causes of death. Early diagnosis of lung nodules has great significance for the prevention of lung cancer. Despite major improvements in modern diagnosis and treatment, the five-year survival rate is only 18%. Before diagnosis, the classification of lung nodules is one important step, in particular, because automatic classification may help doctors with a valuable opinion. Although deep learning has shown improvement in the image classifications over traditional approaches, which focus on handcraft features, due to a large number of intra-class variational images and the inter-class similar images due to various imaging modalities, it remains challenging to classify lung nodule. In this paper, a multi-deep model (MD model) is proposed for lung nodule classification as well as to predict the image label class. This model is based on three phases that include multi-scale dilated convolutional blocks (MsDc), dual deep convolutional neural networks (DCNN A/B), and multi-task learning component (MTLc). Initially, the multi-scale features are derived through the MsDc process by using different dilated rates to enlarge the respective area. This technique is applied to a pair of images. Such images are accepted by dual DCNNs, and both models can learn mutually from each other in order to enhance the model accuracy. To further improve the performance of the proposed model, the output from both DCNNs split into two portions. The multi-task learning part is used to evaluate whether the input image pair is in the same group or not and also helps to classify them between benign and malignant. Furthermore, it can provide positive guidance if there is an error. Both the intra-class and inter-class (variation and similarity) of a dataset itself increase the efficiency of single DCNN. The effectiveness of mentioned technique is tested empirically by using the popular Lung Image Consortium Database (LIDC) dataset. The results show that the strategy is highly efficient in the form of sensitivity of 90.67%, specificity 90.80%, and accuracy of 90.73%
PORIFERAL VISION: Deep Transfer Learning-based Sponge Spicules Identification & Taxonomic Classification
The phylum Porifera includes the aquatic organisms known as sponges. Sponges are classified into four classes: Calcarea, Hexactinellida, Demospongiae, and Homoscleromorpha. Within Demospongiae and Hexactinellida, sponges’ skeletons are needle-like spicules made of silica. With a wide variety of shapes and sizes, these siliceous spicules’ morphology plays a pivotal role in assessing and understanding sponges\u27 taxonomic diversity and evolution. In marine ecosystems, when sponges die their bodies disintegrate over time, but their spicules remain in the sediments as fossilized records that bear ample taxonomic information to reconstruct the evolution of sponge communities and sponge phylogeny.
Traditional methods of identifying spicules from core samples of marine sediments are labor-intensive and cannot scale to the scope needed for large analysis. Through the incorporation of high-throughput microscopy and deep learning, image classification has made significant strides toward automating the task of species recognition and taxonomic classification. Even with sparse training data and highly specific image domains, deep convolutional neural networks (DCNNs) were able to extract taxonomic features among morphologically diverse microfossils. Using transfer learning, training a classifier on pretrained DCNNs has achieved recent successes in classifying similar microfossils, such as diatom frustules and radiolarian skeletons.
In this project, I address the reliability of pretrained models to perform spicule identification and class-level classification. Using FlowCam technology to photograph individual microparticles, our dataset consists of spicule and non-spicule types without additional image segmentation and augmentation. Our proposed method is a pre-trained model with a custom classifier that performs two different binary classifications: a spicule vs non-spicule classification, and a taxonomic classification of Demospongiae vs. Hexactinellida. We evaluate the effect of implementing different DCNN architectures, data set sizes, and classifiers on image classification performance. Surprisingly, MobileNet, a relatively new and small architecture, showed the best performance while still being the most computationally efficient.
Other studies that didn’t involve MobileNet had similar high accuracies for multi-class classifications with fewer training images. The reliability of DCNNs for binary spicule classification implicates the promising approach of a more nuanced multi-class/taxonomic classification. Future work should build multi-class classification that ranges more biogenic materials for the identification or more sponge taxonomic levels for species classification
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