AY
A.D. Ye
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Optimising Labeling
The limits of weakly supervised osteophytes severity grading and localization in Hip X-Rays
Osteophytes are bony protrusions that are key radiographic indicators of hip os-
teoarthritis (OA), but grading their severity in specific hip locations is a time consum-
ing process that requires an expert. In many cases it is expensive to scale datasets
with location annotated severity labelling by experts, where as weak labels, containing
only the global presence of osteophytes is much easier to attain. This paper investi-
gates whether such weak global label can improve localized severity grading through a
multitask deep learning framework.
We study a ResNet-18 based convolutional network that shares and updates its
weights across two output heads, a global binary classification head and four regional
ordinal heads for femur superior, femur inferior, acetabulum superior and acetabulum
inferior. The model is trained under four supervision strategies: a strong-only config-
uration using only quadrant-level labels, a masked baseline that incorporates weakly
labelled negatives via label propagation and ignores weak positives in the local loss,
and two Multi-Instance Learning variants that use a Noisy-OR loss to propagate weak
positive labels to the quadrants. We systematically vary the ratio of weak to strong la-
bels and evaluate performance using quadratic weighted Cohen’s kappa as the primary
metric.
Experiments show that the masked baseline with weak labels improves regional
kappa score compared to the strong-only configuration, while MIL variants fail to out-
perform the baseline and can degrade performance at higher weak-to-strong ratios. We
further observe that selecting checkpoints by minimal joint validation loss underesti-
mates achievable kappa score, due to faster convergence of the global task, whereas
selecting by maximal kappa score yields substantially better localized grading. Overall
the findings highlight the trade off between localization and classification performance
in weakly supervised multitask learning pipelines for regional osteophytes grading in
hip X-Rays. ...
teoarthritis (OA), but grading their severity in specific hip locations is a time consum-
ing process that requires an expert. In many cases it is expensive to scale datasets
with location annotated severity labelling by experts, where as weak labels, containing
only the global presence of osteophytes is much easier to attain. This paper investi-
gates whether such weak global label can improve localized severity grading through a
multitask deep learning framework.
We study a ResNet-18 based convolutional network that shares and updates its
weights across two output heads, a global binary classification head and four regional
ordinal heads for femur superior, femur inferior, acetabulum superior and acetabulum
inferior. The model is trained under four supervision strategies: a strong-only config-
uration using only quadrant-level labels, a masked baseline that incorporates weakly
labelled negatives via label propagation and ignores weak positives in the local loss,
and two Multi-Instance Learning variants that use a Noisy-OR loss to propagate weak
positive labels to the quadrants. We systematically vary the ratio of weak to strong la-
bels and evaluate performance using quadratic weighted Cohen’s kappa as the primary
metric.
Experiments show that the masked baseline with weak labels improves regional
kappa score compared to the strong-only configuration, while MIL variants fail to out-
perform the baseline and can degrade performance at higher weak-to-strong ratios. We
further observe that selecting checkpoints by minimal joint validation loss underesti-
mates achievable kappa score, due to faster convergence of the global task, whereas
selecting by maximal kappa score yields substantially better localized grading. Overall
the findings highlight the trade off between localization and classification performance
in weakly supervised multitask learning pipelines for regional osteophytes grading in
hip X-Rays. ...
Osteophytes are bony protrusions that are key radiographic indicators of hip os-
teoarthritis (OA), but grading their severity in specific hip locations is a time consum-
ing process that requires an expert. In many cases it is expensive to scale datasets
with location annotated severity labelling by experts, where as weak labels, containing
only the global presence of osteophytes is much easier to attain. This paper investi-
gates whether such weak global label can improve localized severity grading through a
multitask deep learning framework.
We study a ResNet-18 based convolutional network that shares and updates its
weights across two output heads, a global binary classification head and four regional
ordinal heads for femur superior, femur inferior, acetabulum superior and acetabulum
inferior. The model is trained under four supervision strategies: a strong-only config-
uration using only quadrant-level labels, a masked baseline that incorporates weakly
labelled negatives via label propagation and ignores weak positives in the local loss,
and two Multi-Instance Learning variants that use a Noisy-OR loss to propagate weak
positive labels to the quadrants. We systematically vary the ratio of weak to strong la-
bels and evaluate performance using quadratic weighted Cohen’s kappa as the primary
metric.
Experiments show that the masked baseline with weak labels improves regional
kappa score compared to the strong-only configuration, while MIL variants fail to out-
perform the baseline and can degrade performance at higher weak-to-strong ratios. We
further observe that selecting checkpoints by minimal joint validation loss underesti-
mates achievable kappa score, due to faster convergence of the global task, whereas
selecting by maximal kappa score yields substantially better localized grading. Overall
the findings highlight the trade off between localization and classification performance
in weakly supervised multitask learning pipelines for regional osteophytes grading in
hip X-Rays.
teoarthritis (OA), but grading their severity in specific hip locations is a time consum-
ing process that requires an expert. In many cases it is expensive to scale datasets
with location annotated severity labelling by experts, where as weak labels, containing
only the global presence of osteophytes is much easier to attain. This paper investi-
gates whether such weak global label can improve localized severity grading through a
multitask deep learning framework.
We study a ResNet-18 based convolutional network that shares and updates its
weights across two output heads, a global binary classification head and four regional
ordinal heads for femur superior, femur inferior, acetabulum superior and acetabulum
inferior. The model is trained under four supervision strategies: a strong-only config-
uration using only quadrant-level labels, a masked baseline that incorporates weakly
labelled negatives via label propagation and ignores weak positives in the local loss,
and two Multi-Instance Learning variants that use a Noisy-OR loss to propagate weak
positive labels to the quadrants. We systematically vary the ratio of weak to strong la-
bels and evaluate performance using quadratic weighted Cohen’s kappa as the primary
metric.
Experiments show that the masked baseline with weak labels improves regional
kappa score compared to the strong-only configuration, while MIL variants fail to out-
perform the baseline and can degrade performance at higher weak-to-strong ratios. We
further observe that selecting checkpoints by minimal joint validation loss underesti-
mates achievable kappa score, due to faster convergence of the global task, whereas
selecting by maximal kappa score yields substantially better localized grading. Overall
the findings highlight the trade off between localization and classification performance
in weakly supervised multitask learning pipelines for regional osteophytes grading in
hip X-Rays.