Paper Presentation

Here are some slides I made to present this MIDL 2021 paper in our reading group:

Just like the last term, I decided to present on a topic spanning 2-3 papers instead of a single paper. This time, I chose to present on how existing works “aggregate” multi-annotator segmentations for medical images. These are the papers that I covered in this presentation: Warfield et al., “Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation”, IEEE Transactions on Medical Imaging, 2004 [URL]. Kats et al., “A Soft STAPLE Algorithm Combined with Anatomical Knowledge”, MICCAI 2019 [URL]. Zhang et al., “Learning to Segment When Experts Disagree”, MICCAI 2020 [URL]. Here are the slides I made to present this topic in our reading group: ...

This term, I really wanted to present this one ICASSP paper I found very interesting, but then I realized that the authors followed up their work with another journal paper (IEEE TIP), so I decided to discuss both. Both of these papers deal with the topic of unsupervised image segmentation: Kanezaki, “Unsupervised Image Segmentation by Backpropagation”, ICASSP 2018 [URL]. Kim et al. “Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering”, IEEE Transactions on Image Processing, 2020 [URL]. Here are the slides I made to present this topic in our reading group: ...

The semantic segmentation task in computer vision involves partitioning an image into a set of multiple non-overlapping and semantically interpretable regions. This entails assigning pixel-wise class labels to the entire image, making it a dense prediction task. Owing to the massive improvements in image classification performance achieved by CNNs over the recent years, there have been several works which successfully repurpose these popular image classification CNN architectures for dense prediction tasks. This paper questions this approach, and instead investigates if modules specifically designed for a dense prediction task would improve the segmentation performance even further. Unlike image classification networks which aggregate multi-scale contextual information through successive downsampling operations to obtain a global prediction, a dense prediction task like semantic segmentation requires “multi-scale contextual reasoning in combination with full-resolution output”. % However, increasing the receptive field of the convolution operator comes at the cost of more parameters. The authors therefore propose using the dilated convolution operator to address this. To this end, this paper makes threefold contributions: (a) a generalized form of the convolution operator to account for dilation, (b) a multi-scale context aggregation module that relies on dilated convolution, and (c) a simiplified front-end module which gets rid of “vestigial components” carried over from image classification networks. ...

Here are some slides I made to present this CVPR 2019 paper in our reading group: