Binesh Sadanandan PhD Dissertation Companion
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Thrust 4 · Chapter 6

Safety

Do consistency improvements produce safer, more image-dependent behavior?

A four-quadrant audit crossing consistency with image reliance shows that, averaged across ten model-dataset settings, 81% of each model's consistent predictions are image-invariant (range 45-100%). Consistency-optimized adapters concentrate predictions in the Dangerous quadrant (consistent but text-reliant), reaching 84.1% for Targeted LoRA on PadChest, though Chapter 6 qualifies that placement: Targeted LoRA also has the highest image-swap sensitivity of the LoRA variants (31.5% on the same 861 questions), so the text-only-agreement criterion can overcount on PadChest, where high finding base rates let the text prior and the image agree. Correctness is a separate axis: on PadChest the text-reliant cell is often more accurate than the grounded one, so the screen flags behavior. It does not flag per-prediction error.

How this thrust was run

Classify each prediction on curated flip banks by paraphrase consistency and text-only agreement into four quadrants, with attention-grounding (true vs shifted box), occlusion faithfulness, and demographic fairness stratification as companion analyses.

Datasets
MIMIC-CXR, PadChest
Models
MedGemma-4B, Targeted LoRA, Full LoRA, LLaVA-Rad, LLaVA-Rad LoRA
Thesis tables
tab:quadrant_counts, tab:grounding, tab:fairness

Confirmed Reducing flips can create a false sense of safety: averaged across ten model-dataset settings, 81% of each model's consistent predictions are image-invariant (range 45-100%), so consistency and grounding routinely come apart.

Negative result No: attention does not reliably indicate grounding. True-box coverage only marginally exceeds a displaced box (0.296 vs 0.261 for Base), and patch-rank correlation between attention and causal occlusion importance is near zero across all three MedGemma variants.

Mixed Partially: the worst-calibrated model also shows the largest demographic disparities. Full LoRA has the worst calibration (ECE of about 0.25 or higher for every group) and the largest sex gaps, while Targeted LoRA has the smallest sex ECE gap (0.012) but the steepest age accuracy gradient (13 pp).

Primary results

81% (range 45-100%)

model-dataset

81% of consistent predictions are image-invariant

Averaged across ten model-dataset settings, 81% of each model's consistent predictions are unchanged when the image is removed (range 45-100%), so a low paraphrase-flip rate is not evidence of grounded visual reasoning.

Multiple models · Multiple datasets · n = 10 · unweighted mean over 10 settings; range 45-100%

Source, denominator, and limits
Metric
mean share of consistent predictions that are image-invariant (text-only agreement)
Denominator
model-dataset (per model-dataset setting)
Sample
n = 10
Model
Multiple models
Dataset
Multiple datasets
Split
eval
Comparison
range 45-100% across the ten model-dataset settings
Uncertainty
unweighted mean over 10 settings; range 45-100%
Thesis
Chapter 6, tab:quadrant_counts
Source artifact
dissertation/tables/thrust4/table_quadrant_counts.tex
Last verified
2026-07-15
Open in Evidence Explorer

84.1%

question-level

Targeted LoRA on PadChest: 84.1% of questions in the Dangerous quadrant

After targeted LoRA adaptation, 84.1% of PadChest questions land in the Dangerous quadrant (consistent under paraphrase but not image-reliant): the adapter buys consistency largely by leaning on the text prior.

Targeted LoRA · PadChest · n = 861

Source, denominator, and limits
Metric
share of questions classified Dangerous (consistent + text-reliant)
Denominator
question-level (per question)
Sample
n = 861
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
Base MedGemma-4B 22.5%, Full LoRA 60.6% on the same 861 PadChest questions
Uncertainty
not reported for this value
Thesis
Chapter 6, tab:quadrant_counts
Source artifact
dissertation/tables/thrust4/table_quadrant_counts.tex
Last verified
2026-07-15
Open in Evidence Explorer

r = -0.15 (conditioned)

model-dataset

The flip-rate vs Dangerous-fraction correlation is largely definitional

The headline r = -0.86 correlation between flip rate and the Consistent-Text-driven fraction is largely a definitional consequence of that fraction being a subset of consistent predictions; conditioning on consistency reduces it to r = -0.15.

Multiple models · Multiple datasets · n = 10 · Spearman -0.13 conditioned; 10 data points

Source, denominator, and limits
Metric
Pearson r between flip rate and image-invariant share of consistent predictions, conditioned on consistency
Denominator
model-dataset (per model-dataset setting)
Sample
n = 10
Model
Multiple models
Dataset
Multiple datasets
Split
eval
Comparison
-0.86 unconditioned
Uncertainty
Spearman -0.13 conditioned; 10 data points
Thesis
Chapter 6, tab:quadrant_counts
Source artifact
dissertation/chapters/05_thrust4.tex
Last verified
2026-07-15
Open in Evidence Explorer

0.296 true box vs 0.261 shifted

case-level

Attention coverage barely beats a shifted box (0.296 vs 0.261)

Base MedGemma-4B places 29.6% of attention mass inside the radiologist-annotated pathology box, only marginally more than the 26.1% it places in a same-sized shifted box: attention is a coarse localizer, short of a faithful one.

MedGemma-4B · PadChest · n = 637

Source, denominator, and limits
Metric
true-box attention coverage vs shifted-box baseline
Denominator
case-level (per case)
Sample
n = 637
Model
MedGemma-4B
Dataset
PadChest
Split
ood
Comparison
shifted-box 0.261; random-far 0.056 (roughly 5-6x above random, marginally above shifted)
Uncertainty
not reported for this value
Thesis
Chapter 6, tab:grounding
Source artifact
dissertation/tables/thrust4/table_grounding.tex
Last verified
2026-07-15
Open in Evidence Explorer

13 pp (83.3% under-40 to 96.3% over-80)

question-level

Targeted LoRA accuracy climbs 13 pp from youngest to oldest patients

Targeted LoRA accuracy on PadChest rises from 83.3% for patients under 40 to 96.3% for patients over 80, a 13 percentage-point age gradient with younger patients least accurate - the steepest of the three MedGemma variants.

Targeted LoRA · PadChest · n = 861

Source, denominator, and limits
Metric
accuracy range across age bins (<40, 40-60, 60-80, 80+)
Denominator
question-level (per question)
Sample
n = 861
Model
Targeted LoRA
Dataset
PadChest
Split
ood
Comparison
Base is nearly flat across age (76.8-81.1%); Full LoRA spans 63.1-71.3% at much lower overall accuracy
Uncertainty
not reported for this value
Thesis
Chapter 6, tab:fairness
Paper
Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
Source artifact
results/uai/fairness_analysis.json
Last verified
2026-07-15
Open in Evidence Explorer

Charts from this thrust

Open any of these in the evidence explorer to filter by model, dataset, or metric.

Four-quadrant safety screen: consistency against image reliance

Ten model-dataset settings on the curated behavioural flip banks: MedGemma variants on MIMIC-CXR n=98 and PadChest n=861; LLaVA-Rad variants on MIMIC-CXR n=88 and PadChest n=732 (its text-only-baseline subset).

Image-swap sensitivity: how often a contradicting image changes the answer

One population only: the 861-question PadChest flip bank, four models (base MedGemma-4B, Targeted LoRA, Full LoRA, LLaVA-Rad). MIMIC-CXR cells are excluded.

Accuracy by patient age band

PadChest flip bank only, n=861 questions per model split into four age bands (under 40 n=48, 40-60 n=233, 60-80 n=416, over 80 n=164), for base MedGemma-4B, Targeted LoRA and Full LoRA.

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