Autofocus Layer for Semantic Segmentation

By Yao Qin, Konstantinos Kamnitsas, Siddharth Ancha, Jay Nanavati, Garrison W. Cottrell, Antonio Criminisi, Aditya V. Nori. 2018

126 citations recorded by the source corpus.

We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing. Autofocus layers adaptively change the size of the effective receptive field based on the processed context to generate more powerful features. This is achieved by parallelising multiple convolutional layers with different dilation rates, combined by an attention mechanism that learns to focus on the optimal scales driven by context. By sharing the weights of the parallel convolutions we make the network scale-invariant, with only a modest increase in the number of parameters. The proposed autofocus layer can be easily integrated into existing networks to improve a model's representational power. We evaluate our models on the challenging tasks of multi-organ segmentation in pelvic CT and brain tumor segmentation in MRI and achieve very promising performance.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Errui Ding
2. Hiroki Tokunaga
3. Yuhui Ma
4. Fei Shi
5. Su Ruan
6. Dehui Xiang
7. Arvid Lundervold
8. Cuntai Guan

In Radiology, Nuclear Medicine and Imaging

1. Errui Ding
2. Arvid Lundervold
3. Cuntai Guan
4. Stéphane Canu
5. Erico Tjoa
6. Florian Tramèr
7. Dan Boneh

Citations in the corpus

• A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, methodology): “The autofocus module from [66] promises improvements in visual interpretability for segmentation on pelvic CT scans and segmentation of tumor in brain MRI using CNN.”
• An overview of deep learning in medical imaging focusing on MRI (2018, background): “Not only medical technology companies, but also for example Google Brain [28–30],4 DeepMind [31],5 Microsoft [32,33]6 and IBM [34].7 There is also a plethora of small and medium-sized businesses in the field.8 2 Machine learning, artificial neural networks, deep learning In machine learning one deve”
• A review: Deep learning for medical image segmentation using multi-modality fusion (2019, methodology): “[56] proposed the autofocus convolutional layer to enhance the abilities of neural networks by using multi-scale processing.”
• ACFNet: Attentional Class Feature Network for Semantic Segmentation (2019, background): “Some other works focus on how to fuse different context information [43, 42, 12, 28] more selec-”
• Adaptive Weighting Multi-Field-Of-View CNN for Semantic Segmentation in Pathology (2019, methodology): “There are several methods that can adaptively weight the effective channels [20], pixels (location) [30], and scales [15][35] in a single network.”
• CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation (2020, methodology): “AFNet [22] and SPAP [29] employed scale-aware layers to adaptively change the sizes of the effective receptive fields.”