Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiple Instance Learning with Auxiliary Task Weighting for Multiple
  Myeloma Classification   Whole body magnetic resonance imaging (WB-MRI) is the recommended modality
for diagnosis of multiple myeloma (MM). WB-MRI is used to detect sites of
disease across the entire skeletal system, but it requires significant
expertise and is time-consuming to report due to the great number of images. To
aid radiological reading, we propose an auxiliary task-based multiple instance
learning approach (ATMIL) for MM classification with the ability to localize
sites of disease. This approach is appealing as it only requires patient-level
annotations where an attention mechanism is used to identify local regions with
active disease. We borrow ideas from multi-task learning and define an
auxiliary task with adaptive reweighting to support and improve learning
efficiency in the presence of data scarcity. We validate our approach on both
synthetic and real multi-center clinical data. We show that the MIL attention
module provides a mechanism to localize bone regions while the adaptive
reweighting of the auxiliary task considerably improves the performance.

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The categories of this paper are: hep-ex
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph