33 research outputs found
InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation
We present a novel, parameter-efficient and practical fully convolutional
neural network architecture, termed InfiNet, aimed at voxel-wise semantic
segmentation of infant brain MRI images at iso-intense stage, which can be
easily extended for other segmentation tasks involving multi-modalities.
InfiNet consists of double encoder arms for T1 and T2 input scans that feed
into a joint-decoder arm that terminates in the classification layer. The
novelty of InfiNet lies in the manner in which the decoder upsamples lower
resolution input feature map(s) from multiple encoder arms. Specifically, the
pooled indices computed in the max-pooling layers of each of the encoder blocks
are related to the corresponding decoder block to perform non-linear
learning-free upsampling. The sparse maps are concatenated with intermediate
encoder representations (skip connections) and convolved with trainable filters
to produce dense feature maps. InfiNet is trained end-to-end to optimize for
the Generalized Dice Loss, which is well-suited for high class imbalance.
InfiNet achieves the whole-volume segmentation in under 50 seconds and we
demonstrate competitive performance against multiple state-of-the art deep
architectures and their multi-modal variants.Comment: 4 pages, 3 figures, conference, IEEE ISBI, 201
Webly Supervised Learning for Skin Lesion Classification
Within medical imaging, manual curation of sufficient well-labeled samples is
cost, time and scale-prohibitive. To improve the representativeness of the
training dataset, for the first time, we present an approach to utilize large
amounts of freely available web data through web-crawling. To handle noise and
weak nature of web annotations, we propose a two-step transfer learning based
training process with a robust loss function, termed as Webly Supervised
Learning (WSL) to train deep models for the task. We also leverage search by
image to improve the search specificity of our web-crawling and reduce
cross-domain noise. Within WSL, we explicitly model the noise structure between
classes and incorporate it to selectively distill knowledge from the web data
during model training. To demonstrate improved performance due to WSL, we
benchmarked on a publicly available 10-class fine-grained skin lesion
classification dataset and report a significant improvement of top-1
classification accuracy from 71.25 % to 80.53 % due to the incorporation of
web-supervision.Comment: Accepted to International Conference on Medical Image Computing and
Computer-Assisted Intervention 2018 Added Acknowledgements section, rest is
unchanged. In MICCAI 2018. Springer, Cha
Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks
Increased information sharing through short and long-range skip connections
between layers in fully convolutional networks have demonstrated significant
improvement in performance for semantic segmentation. In this paper, we propose
Competitive Dense Fully Convolutional Networks (CDFNet) by introducing
competitive maxout activations in place of naive feature concatenation for
inducing competition amongst layers. Within CDFNet, we propose two
architectural contributions, namely competitive dense block (CDB) and
competitive unpooling block (CUB) to induce competition at local and global
scales for short and long-range skip connections respectively. This extension
is demonstrated to boost learning of specialized sub-networks targeted at
segmenting specific anatomies, which in turn eases the training of complex
tasks. We present the proof-of-concept on the challenging task of whole body
segmentation in the publicly available VISCERAL benchmark and demonstrate
improved performance over multiple learning and registration based
state-of-the-art methods.Comment: Paper accepted on MICCAI-MLMI 2018 worksho