2 research outputs found
Weakly Supervised Clustering by Exploiting Unique Class Count
A weakly supervised learning based clustering framework is proposed in this
paper. As the core of this framework, we introduce a novel multiple instance
learning task based on a bag level label called unique class count (),
which is the number of unique classes among all instances inside the bag. In
this task, no annotations on individual instances inside the bag are needed
during training of the models. We mathematically prove that with a perfect
classifier, perfect clustering of individual instances inside the bags is
possible even when no annotations on individual instances are given during
training. We have constructed a neural network based classifier and
experimentally shown that the clustering performance of our framework with our
weakly supervised classifier is comparable to that of fully supervised
learning models where labels for all instances are known. Furthermore, we have
tested the applicability of our framework to a real world task of semantic
segmentation of breast cancer metastases in histological lymph node sections
and shown that the performance of our weakly supervised framework is comparable
to the performance of a fully supervised Unet model.Comment: Published as a conference paper at ICLR 202
Studying The Effect of MIL Pooling Filters on MIL Tasks
There are different multiple instance learning (MIL) pooling filters used in
MIL models. In this paper, we study the effect of different MIL pooling filters
on the performance of MIL models in real world MIL tasks. We designed a neural
network based MIL framework with 5 different MIL pooling filters: `max',
`mean', `attention', `distribution' and `distribution with attention'. We also
formulated 5 different MIL tasks on a real world lymph node metastases dataset.
We found that the performance of our framework in a task is different for
different filters. We also observed that the performances of the five pooling
filters are also different from task to task. Hence, the selection of a correct
MIL pooling filter for each MIL task is crucial for better performance.
Furthermore, we noticed that models with `distribution' and `distribution with
attention' pooling filters consistently perform well in almost all of the
tasks. We attribute this phenomena to the amount of information captured by
`distribution' based pooling filters. While point estimate based pooling
filters, like `max' and `mean', produce point estimates of distributions,
`distribution' based pooling filters capture the full information in
distributions. Lastly, we compared the performance of our neural network model
with `distribution' pooling filter with the performance of the best MIL methods
in the literature on classical MIL datasets and our model outperformed the
others.Comment: 16 page