39 research outputs found
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
Chest X-ray is one of the most accessible medical imaging technique for
diagnosis of multiple diseases. With the availability of ChestX-ray14, which is
a massive dataset of chest X-ray images and provides annotations for 14
thoracic diseases; it is possible to train Deep Convolutional Neural Networks
(DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we
experiment a set of deep learning models and present a cascaded deep neural
network that can diagnose all 14 pathologies better than the baseline and is
competitive with other published methods. Our work provides the quantitative
results to answer following research questions for the dataset: 1) What loss
functions to use for training DCNN from scratch on ChestX-ray14 dataset that
demonstrates high class imbalance and label co occurrence? 2) How to use
cascading to model label dependency and to improve accuracy of the deep
learning model?Comment: Submitted to CVPR 201
A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification
This paper analyzes and compares different deep learning loss functions in
the framework of multi-label remote sensing (RS) image scene classification
problems. We consider seven loss functions: 1) cross-entropy loss; 2) focal
loss; 3) weighted cross-entropy loss; 4) Hamming loss; 5) Huber loss; 6)
ranking loss; and 7) sparseMax loss. All the considered loss functions are
analyzed for the first time in RS. After a theoretical analysis, an
experimental analysis is carried out to compare the considered loss functions
in terms of their: 1) overall accuracy; 2) class imbalance awareness (for which
the number of samples associated to each class significantly varies); 3)
convexibility and differentiability; and 4) learning efficiency (i.e.,
convergence speed). On the basis of our analysis, some guidelines are derived
for a proper selection of a loss function in multi-label RS scene
classification problems.Comment: Accepted at IEEE International Geoscience and Remote Sensing
Symposium (IGARSS) 2020. For code visit:
https://gitlab.tubit.tu-berlin.de/rsim/RS-MLC-Losse
ELASTIC: Improving CNNs with Dynamic Scaling Policies
Scale variation has been a challenge from traditional to modern approaches in
computer vision. Most solutions to scale issues have a similar theme: a set of
intuitive and manually designed policies that are generic and fixed (e.g. SIFT
or feature pyramid). We argue that the scaling policy should be learned from
data. In this paper, we introduce ELASTIC, a simple, efficient and yet very
effective approach to learn a dynamic scale policy from data. We formulate the
scaling policy as a non-linear function inside the network's structure that (a)
is learned from data, (b) is instance specific, (c) does not add extra
computation, and (d) can be applied on any network architecture. We applied
ELASTIC to several state-of-the-art network architectures and showed consistent
improvement without extra (sometimes even lower) computation on ImageNet
classification, MSCOCO multi-label classification, and PASCAL VOC semantic
segmentation. Our results show major improvement for images with scale
challenges. Our code is available here: https://github.com/allenai/elasticComment: CVPR 2019 oral, code available https://github.com/allenai/elasti