Deep learning models often achieve expert-level accuracy in medical image classification but suffer from a critical flaw: semantic incoherence. These high-confidence mistakes that are semantically incoherent (e.g., classifying a malignant tumor as benign) fundamentally differ from acceptable errors which stem from visual ambiguity. Unlike safe, fine-grained disagreements, these fatal failures erode clinical trust. To address this, we propose Risk-Calibrated Learning, a technique that explicitly distinguishes between visual ambiguity (fine-grained errors) and catastrophic structural errors. By embedding a confusion-aware clinical severity matrix M into the optimization landscape, our method suppresses critical errors (false negatives) without requiring complex architectural changes. We validate our approach in four different imaging modalities: Brain Tumor MRI, ISIC 2018 (Dermoscopy), BreaKHis (Breast Histopathology), and SICAPv2 (Prostate Histopathology). Extensive experiments demonstrate that our Risk-Calibrated Loss consistently reduces the Critical Error Rate (CER) for all four datasets, achieving relative safety improvements ranging from 20.0% (on breast histopathology) to 92.4% (on prostate histopathology) compared to state-of-the-art baselines such as Focal Loss. These results confirm that our method offers a superior safety-accuracy trade-off across both CNN and Transformer architectures.
Figures & tables
Method
Optimization Focus
Penalty Type
Safety-Aware
Standard CE [ 3 ]
Accuracy
Uniform
No
Focal Loss [ 4 ]
Hard Samples
Dynamic (Confidence)
No
HXE [ 7 ]
Taxonomy
Symmetric Distance
Partially
Ours (RCL)
Clinical Risk
Asymmetric (Semantic)
Yes
TABLE I: Comparison of Baseline Loss Functions vs. RCL.
Fig. 1: The Spectrum of Errors (BreaKHis dataset). (a) Visual Ambiguity: Confusing visual lookalikes (e.g., Adenosis vs. Fibroadenoma) is acceptable. (b) Type I (Costly): A False Alarm where benign tissue is flagged as cancer. (c) Type II (Fatal): A catastrophic failure where an obvious Mucinous Carcinoma is classified as a Benign Adenoma.
TABLE III: Comparative Performance of Risk-Calibrated Loss (RCL) vs. State-of-the-Art Baselines. We report the Proposed configuration ( α=5,β=20 ) as RCL. CER denotes Critical Error Rate (Lower is Better). Acc denotes Standard Fine-Grained Accuracy. Best Safety results are in bold .
Fig. 2: Safety vs. Accuracy Trade-off (ISIC 2018, ResNet-50). The scatter plot compares RCL against standard baselines (CE, WCE, Focal, LS). The X-axis represents the overall F1-Macro score (higher is better), while the Y-axis represents the Critical Error Rate (CER, lower is safer). Each data point corresponds to a specific loss function’s performance. The Focal Loss baseline (Red) remains in the high-risk, high-accuracy zone, whereas the RCL-Proposed method (Green) explicitly shifts the model to the safe Top-Left corner, prioritizing the reduction of fatal errors over marginal gains in fine-grained accuracy.
Configuration
α
β
Motivation / Hypothesis
RCL-Light
5.0
5.0
Baseline check for stability (accuracy)
RCL-Balanced
2.0
10.0
Attempt to recover fine-grained accuracy
RCL-StructSafe
5.0
10.0
Stronger structure, milder safety constraint
RCL-Uniform
10.0
10.0
Coarse, symmetric high penalty
RCL-Sparse
1.0
20.0
Targeted solely on Critical Errors
RCL-Proposed
5.0
20.0
Optimal trade-off (Structure + Safety)
TABLE IV: Hyperparameter Configurations for Ablation Study. α defines the Type I Penalty (False Positive); β defines the Type II Penalty (Fatal).
Fig. 3: Ablation Study on SICAPv2 (ViT-B16). The bar chart illustrates the impact of different penalty configurations on model safety. The X-axis categorizes the tested loss configurations, while the Y-axis measures the Critical Error Rate (CER). The Staircase to Safety trend demonstrates that while a Uniform configuration ( α=10,β=10 ) reduces some errors relative to the baseline, only the Proposed configuration ( α=5,β=20 ) effectively eliminates fatal errors (achieving a CER < 1%).
Method / Configuration
α
β
CER ( ↓ )
F1-Macro ( ↑ )
Standard CE [ 3 ]
-
-
39.09%
0.649
Label Smoothing (LS) [ 5 ]
-
-
38.44%
0.669
Weighted CE (WCE) [ 3 ]
-
-
25.73%
0.633
Baseline (Focal) [ 4 ]
-
-
39.41%
0.692
RCL-Uniform
10
10
32.57%
0.650
RCL-Sparse
1
20
3.58%
0.455
TABLE V: Baseline Comparison and Ablation Study (ISIC 2018, ResNet-50).