Adaptive Comprehensive Focal Loss (ACFL)

• Adaptive Comprehensive Focal Loss (ACFL) is a framework of adaptive focal mechanisms that adjust loss weighting based on training progress, pixel difficulty, and task-specific signals.
• It integrates variants like Automated Focal Loss and AdaptiveClick, employing techniques such as gradient normalization, hard-region masks, and adaptive modulation to enhance performance.
• ACFL approaches have demonstrated improved convergence, enhanced boundary fidelity, and increased accuracy across object detection, interactive segmentation, and medical image analysis.

Adaptive Comprehensive Focal Loss (ACFL) denotes a class of adaptive focal-style objectives rather than a single universally standardized loss. Across the cited literature, the shared core is the focal-loss modulation of cross-entropy by a hardness-dependent factor, but the fixed focusing parameter is replaced or augmented by mechanisms that depend on training progress, pixel difficulty, gradient balance, hard-region masks, annotation variability, or other task-specific signals. The exact acronym ACFL is not used as the formal name of the principal formulations surveyed here: AdaptiveClick introduces Adaptive Focal Loss (AFL), Automated Focal Loss adapts $\gamma$ from expected correctness, and a micro-ultrasound segmentation study presents a custom adaptive focal loss that is explicitly described as ACFL-like in spirit rather than as a canonical ACFL (Lin et al., 2023, Weber et al., 2019, Fatema et al., 19 Sep 2025).

1. Conceptual scope and mathematical backbone

The baseline object from which ACFL-like methods depart is focal loss. In the pixel-wise notation used for interactive segmentation, one defines

$$\mathbf{P}^{i}_{\mathrm{t}}= \begin{cases} \mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=1,\ 1-\mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=0, \end{cases}$$

with binary cross-entropy

$$\mathbb{\ell }_{\mathrm{bce}} = -\sum_{i=1}^{\mathcal{N}} \log\left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$

and focal loss

$$\mathbb{\ell }_{\mathrm{fl}} = -\sum_{i=1}^{\mathcal{N}} \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log \left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$

where $\gamma \in [0,5]$ controls the suppression of easy pixels. AdaptiveClick further places Poly Loss in the same algebraic family,

$$\mathbb{\ell }_{\mathrm{pl}} = -\sum_{i=1}^{\mathcal{N}} \left[\left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log\left(\mathbf{P}^{i}_{\mathrm{t}}\right) + \alpha \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma+1}\right],$$

and then generalizes this structure with adaptive coefficients that alter both the exponent and the effective gradient field (Lin et al., 2023).

Within this literature, “comprehensive” is best treated descriptively rather than as a settled formal keyword. The methods do not merely reweight easy and hard examples through a fixed $\gamma$; they integrate additional control variables such as adaptive difficulty estimation, gradient normalization, hard-region priors, and auxiliary mask losses. This suggests that ACFL is more accurately understood as an umbrella descriptor for generalized adaptive focal mechanisms than as a single equation with one authoritative parameterization.

2. Training-progress adaptation and automated focusing

A foundational adaptive variant is Automated Focal Loss, which starts from the standard focused form

$$L_{focused} = w \cdot L,\qquad w = (1-p_{correct})^\gamma,$$

and replaces the fixed $\gamma$ with a training-dependent quantity computed from the expected probability of correct prediction, $\hat{p}_{correct}$. The paper estimates $$\mathbf{P}^{i}_{\mathrm{t}}= \begin{cases} \mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=1,\ 1-\mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=0, \end{cases}$$0 as the batch mean of $$\mathbf{P}^{i}_{\mathrm{t}}= \begin{cases} \mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=1,\ 1-\mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=0, \end{cases}$$1 and recommends exponential smoothing,

$$\mathbf{P}^{i}_{\mathrm{t}}= \begin{cases} \mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=1,\ 1-\mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=0, \end{cases}$$2

It then proposes two adaptive definitions. The quantile-based version is

$$\mathbf{P}^{i}_{\mathrm{t}}= \begin{cases} \mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=1,\ 1-\mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=0, \end{cases}$$3

while the preferred information-theoretic rule is

$$\mathbf{P}^{i}_{\mathrm{t}}= \begin{cases} \mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=1,\ 1-\mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=0, \end{cases}$$4

As $$\mathbf{P}^{i}_{\mathrm{t}}= \begin{cases} \mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=1,\ 1-\mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=0, \end{cases}$$5 increases during training, $$\mathbf{P}^{i}_{\mathrm{t}}= \begin{cases} \mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=1,\ 1-\mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=0, \end{cases}$$6 decreases toward $$\mathbf{P}^{i}_{\mathrm{t}}= \begin{cases} \mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=1,\ 1-\mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=0, \end{cases}$$7, so the focusing strength relaxes instead of remaining fixed throughout optimization (Weber et al., 2019).

The same work extends the focal principle to regression by defining a probability of being correct from prediction error relative to label uncertainty, then using

$$\mathbf{P}^{i}_{\mathrm{t}}= \begin{cases} \mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=1,\ 1-\mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=0, \end{cases}$$8

This is not yet “comprehensive” in the later segmentation sense, but it establishes a central ACFL motif: the focusing mechanism should respond to model state rather than being hand-tuned once and frozen. In object detection, the authors explicitly motivate this by the fact that the set of hard and easy examples changes over training, so a fixed $$\mathbf{P}^{i}_{\mathrm{t}}= \begin{cases} \mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=1,\ 1-\mathbf{P}^{i}_{\mathrm{n}}, & \text{if } \mathbf{y}^{i}_{\mathrm{t}}=0, \end{cases}$$9 cannot track the evolving hardness distribution.

3. Pixel-, region-, and gradient-adaptive formulations

AdaptiveClick develops the most explicit generalized focal formulation among the cited works. Its motivation arises from interactive image segmentation under two ambiguity regimes: inter-class click ambiguity, addressed mainly by architecture, and intra-class click ambiguity, where pixels near boundaries or low-confidence regions remain difficult. The authors argue that BCE is difficulty-equal and focal loss is difficulty-oriented, but that in interactive segmentation focal loss can over-focus on extremely hard pixels while suppressing low-confidence easy pixels that are still ambiguous. To address this, AdaptiveClick introduces Adaptive Difficulty Adjustment (ADA) and Adaptive Gradient Representation (AGR). ADA defines

$$\mathbb{\ell }_{\mathrm{bce}} = -\sum_{i=1}^{\mathcal{N}} \log\left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$0

and AGR defines

$$\mathbb{\ell }_{\mathrm{bce}} = -\sum_{i=1}^{\mathcal{N}} \log\left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$1

The resulting AFL is

$$\mathbb{\ell }_{\mathrm{bce}} = -\sum_{i=1}^{\mathcal{N}} \log\left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$2

The paper explicitly states that BCE, Focal Loss, and PolyLoss arise as special cases under particular settings of $$\mathbb{\ell }_{\mathrm{bce}} = -\sum_{i=1}^{\mathcal{N}} \log\left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$3, $$\mathbb{\ell }_{\mathrm{bce}} = -\sum_{i=1}^{\mathcal{N}} \log\left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$4, $$\mathbb{\ell }_{\mathrm{bce}} = -\sum_{i=1}^{\mathcal{N}} \log\left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$5, and $$\mathbb{\ell }_{\mathrm{bce}} = -\sum_{i=1}^{\mathcal{N}} \log\left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$6 (Lin et al., 2023).

The same paper embeds AFL into a larger segmentation system. AdaptiveClick comprises data embedding, a ViT backbone, PMMD, CAMD, and AFL-based loss optimization. AFL is used in the mask loss,

$$\mathbb{\ell }_{\mathrm{bce}} = -\sum_{i=1}^{\mathcal{N}} \log\left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$7

and the total loss is

$$\mathbb{\ell }_{\mathrm{bce}} = -\sum_{i=1}^{\mathcal{N}} \log\left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$8

The fixed hyperparameters reported for AFL are $$\mathbb{\ell }_{\mathrm{bce}} = -\sum_{i=1}^{\mathcal{N}} \log\left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$9, $$\mathbb{\ell }_{\mathrm{fl}} = -\sum_{i=1}^{\mathcal{N}} \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log \left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$0, and $$\mathbb{\ell }_{\mathrm{fl}} = -\sum_{i=1}^{\mathcal{N}} \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log \left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$1, with $$\mathbb{\ell }_{\mathrm{fl}} = -\sum_{i=1}^{\mathcal{N}} \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log \left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$2, $$\mathbb{\ell }_{\mathrm{fl}} = -\sum_{i=1}^{\mathcal{N}} \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log \left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$3, $$\mathbb{\ell }_{\mathrm{fl}} = -\sum_{i=1}^{\mathcal{N}} \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log \left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$4, $$\mathbb{\ell }_{\mathrm{fl}} = -\sum_{i=1}^{\mathcal{N}} \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log \left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$5, and query count $$\mathbb{\ell }_{\mathrm{fl}} = -\sum_{i=1}^{\mathcal{N}} \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log \left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$6.

A different segmentation-specific adaptive focal formulation appears in prostate capsule segmentation from micro-ultrasound images. Here the architecture is a TransUNet-based encoder-decoder segmentation model with multi-scale deep supervision at scales $$\mathbb{\ell }_{\mathrm{fl}} = -\sum_{i=1}^{\mathcal{N}} \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log \left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$7, $$\mathbb{\ell }_{\mathrm{fl}} = -\sum_{i=1}^{\mathcal{N}} \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log \left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$8, and $$\mathbb{\ell }_{\mathrm{fl}} = -\sum_{i=1}^{\mathcal{N}} \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log \left(\mathbf{P}^{i}_{\mathrm{t}}\right),$$9. The adaptive focal mechanism is region-aware rather than gradient-theoretic. Hard regions are identified by annotation disagreement,

$\gamma \in [0,5]$0

then dilated,

$\gamma \in [0,5]$1

with easy region

$\gamma \in [0,5]$2

The adaptive scalar is defined from sample difficulty and annotation variability,

$\gamma \in [0,5]$3

Using standard focal loss

$\gamma \in [0,5]$4

the method computes

$\gamma \in [0,5]$5

then reweights them as

$\gamma \in [0,5]$6

and normalizes by the number of pixels. The paper explicitly states that the method is not named ACFL, but is ACFL-like in spirit because it combines focal-loss focusing, region-aware weighting, hard/easy partitioning, annotation variability, and adaptive modulation (Fatema et al., 19 Sep 2025).

4. Class-level adaptation as a related but distinct line

OWAdapt occupies adjacent conceptual territory but is not itself a focal-loss variant. It redefines cross-entropy at the class-loss level by first computing class-specific losses

$\gamma \in [0,5]$7

or, on a mini-batch of size $\gamma \in [0,5]$8,

$\gamma \in [0,5]$9

then applying an Ordered Weighted Average to the vector $$\mathbb{\ell }_{\mathrm{pl}} = -\sum_{i=1}^{\mathcal{N}} \left[\left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log\left(\mathbf{P}^{i}_{\mathrm{t}}\right) + \alpha \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma+1}\right],$$0: $$\mathbb{\ell }_{\mathrm{pl}} = -\sum_{i=1}^{\mathcal{N}} \left[\left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log\left(\mathbf{P}^{i}_{\mathrm{t}}\right) + \alpha \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma+1}\right],$$1 where $$\mathbb{\ell }_{\mathrm{pl}} = -\sum_{i=1}^{\mathcal{N}} \left[\left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log\left(\mathbf{P}^{i}_{\mathrm{t}}\right) + \alpha \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma+1}\right],$$2 is the $$\mathbb{\ell }_{\mathrm{pl}} = -\sum_{i=1}^{\mathcal{N}} \left[\left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log\left(\mathbf{P}^{i}_{\mathrm{t}}\right) + \alpha \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma+1}\right],$$3-th largest class loss and the weights satisfy $$\mathbb{\ell }_{\mathrm{pl}} = -\sum_{i=1}^{\mathcal{N}} \left[\left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log\left(\mathbf{P}^{i}_{\mathrm{t}}\right) + \alpha \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma+1}\right],$$4, $$\mathbb{\ell }_{\mathrm{pl}} = -\sum_{i=1}^{\mathcal{N}} \left[\left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log\left(\mathbf{P}^{i}_{\mathrm{t}}\right) + \alpha \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma+1}\right],$$5. The adaptive behavior comes from re-sorting the class losses at every iteration, so the classes with larger current errors receive higher-ranked OWA weights. The weighting vector is generated from linguistic quantifiers through

$$\mathbb{\ell }_{\mathrm{pl}} = -\sum_{i=1}^{\mathcal{N}} \left[\left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log\left(\mathbf{P}^{i}_{\mathrm{t}}\right) + \alpha \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma+1}\right],$$6

with Basic, Quadratic, and Exponential quantifiers, and the paper reports that the exponential quantifier performed best in about 80% of cases, with larger $$\mathbb{\ell }_{\mathrm{pl}} = -\sum_{i=1}^{\mathcal{N}} \left[\left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log\left(\mathbf{P}^{i}_{\mathrm{t}}\right) + \alpha \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma+1}\right],$$7 values, especially $$\mathbb{\ell }_{\mathrm{pl}} = -\sum_{i=1}^{\mathcal{N}} \left[\left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log\left(\mathbf{P}^{i}_{\mathrm{t}}\right) + \alpha \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma+1}\right],$$8, often best (Maldonado et al., 2023).

The significance of OWAdapt for ACFL is mainly taxonomic. Both OWAdapt and focal-style adaptive losses de-emphasize easy cases and emphasize hard cases, but they operate on different objects. Focal loss and ACFL-like methods are typically instance-level, pixel-level, or region-level; OWAdapt is explicitly class-level. The paper is equally explicit about a limitation: because its weighting scheme is class-based rather than instance-based, OWAdapt does not address sample-level noise or label corruption directly. This boundary helps separate ACFL-like formulations from the broader space of adaptive reweighting losses.

5. Reported empirical behavior

The empirical literature reports gains in several distinct regimes: one-stage object detection, interactive image segmentation, medical image segmentation, and general multiclass classification. The metrics are task-specific, so the numerical results are not directly interchangeable, but the recurring pattern is that adaptivity is used to improve convergence, hard-case sensitivity, worst-class behavior, or boundary fidelity.

Formulation	Reported results	Context
Automated Focal Loss (Weber et al., 2019)	COCO: AP 30.38, AP$$\mathbb{\ell }_{\mathrm{pl}} = -\sum_{i=1}^{\mathcal{N}} \left[\left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma}\log\left(\mathbf{P}^{i}_{\mathrm{t}}\right) + \alpha \left(1-\mathbf{P}^{i}_{\mathrm{t}}\right)^{\gamma+1}\right],$$9 51.18, convergence in 30 h; KITTI: AOS 37.3, top-down AP 25.0 with classification + regression	object detection and 3D vehicle detection
AdaptiveClick AFL (Lin et al., 2023)	average gains about 0.014–0.07 on NoC85 and 1.136–2.504 on NoC90; fewer clicks for the same IoU	interactive image segmentation
Micro-US adaptive focal loss (Fatema et al., 19 Sep 2025)	Mean Dice 0.9400, Mean HD95 1.9487 mm	prostate capsule segmentation
OWAdapt (Maldonado et al., 2023)	best average rank; Friedman tests with Iman-Davenport correction reject equal performance with $\gamma$0 for all four metrics	image classification

In Automated Focal Loss, the reproduced original focal baseline on COCO is reported as AP 30.41, AP$\gamma$1 46.58, convergence in 44 h, while the adaptive method with focal regression yields AP 30.38, AP$\gamma$2 51.18, and 30 h convergence. The paper also reports that the information-based adaptive $\gamma$3 starts above 6 early in training, settles around 2.2 for much of training, and then declines toward 0 as AP plateaus. On KITTI, automated focal classification improves AOS over normal loss, $\gamma$4-balancing, and multiloss, and adding focal regression increases AOS from 37.0 to 37.3 (Weber et al., 2019).

In AdaptiveClick, AFL is reported to outperform BCE, WBCE, Balanced CE, Soft IoU, FL, NFL, and PolyLoss on SBD and COCO-LVIS training, and the convergence plots show that AFL generally requires fewer clicks for the same IoU. The authors also report that plugging AFL into several established IIS methods yields consistent gains, with particularly strong benefits on transformer-based methods. In the micro-ultrasound study, adaptive focal loss slightly exceeds AG-BCE in both Dice and HD95, and improves over PyTorch focal loss from 0.9140 to 0.9400 in mean Dice and from 2.4973 mm to 1.9487 mm in mean HD95. OWAdapt, although not focal, is evaluated on 13 benchmark image-classification datasets using ResNet50, MobileNet-v2, and VGG19-bn, and is reported as best overall in average rank and mean performance for Accuracy, Macro F1, Minimum class recall, and Minimum class F1 (Lin et al., 2023, Fatema et al., 19 Sep 2025, Maldonado et al., 2023).

6. Limitations, misconceptions, and open directions

A frequent misconception is that ACFL denotes a single established formula. The cited record does not support that interpretation. One paper explicitly says its method is not named ACFL but is only ACFL-like in spirit, while another states that the exact name in the paper is AFL, not ACFL. The safest usage is therefore generic: ACFL refers to adaptive, broadened focal-loss designs, not to one universally fixed objective (Fatema et al., 19 Sep 2025, Lin et al., 2023).

A second misconception is that all adaptive focal methods solve the same problem. The adaptation target varies sharply across works. Automated Focal Loss adapts to training progress through $\gamma$5. AdaptiveClick adapts to hard-pixel confidence and gradient balance in the presence of click ambiguity. The micro-ultrasound method adapts to region difficulty and annotation variability through hard-region masks and dilation. OWAdapt, by contrast, adapts to class-level difficulty rather than per-instance hardness. This suggests that method comparison should be stratified by adaptation level—training-state, class, region, or pixel—rather than by the word “adaptive” alone (Weber et al., 2019, Maldonado et al., 2023).

The limitations reported in the source papers are correspondingly heterogeneous. OWAdapt does not address sample-level noise or label corruption directly. AdaptiveClick reports that too small $\gamma$6 weakens AGR, while too large $\gamma$7 or $\gamma$8 causes fluctuation. Automated Focal Loss notes that the quantile-based adaptive rule still depends on the hyperparameter $\gamma$9, which weakens the goal of hyperparameter elimination, and therefore prefers the information-based rule. The micro-ultrasound study lists increased computational complexity, dependence on accurate estimation of sample difficulty, the possibility that the method may not help much on low-variability datasets, lack of established generalization to other modalities, and the fact that the dilation idea was suggested as future work but not deeply validated (Lin et al., 2023, Weber et al., 2019, Fatema et al., 19 Sep 2025).

A plausible implication is that the future of ACFL-like design lies less in inventing yet another focal exponent and more in matching the adaptation mechanism to the dominant source of uncertainty: evolving hardness during optimization, worst-class underperformance, ambiguous boundaries, or annotation disagreement. The existing literature already spans all four of these regimes, but it does so through distinct formulations rather than through a single canonical ACFL.