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REVIEW 4 major objections 8 minor 108 references

Self-CephaloNet: A Two-stage Novel Framework using Operational Neural Network for Cephalometric Analysis

T0 review · 4 major / 8 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Two-stage AI net hits 82.25% on 2 mm dental landmark test

desk verdict A reasonable incremental architecture for cephalometric landmarking whose headline 82.25% SDR is undercut by using Test1 and Test2 as mutual validation sets; the PKU external validation is the most solid contribution. read the letter →

arxiv 2501.10984 v1 pith:6B3IIURV submitted 2025-01-19 cs.CV math.OC

Md. Shaheenur Islam Sumon , Khandaker Reajul Islam , Tanzila Rafique , Gazi Shamim Hassan , Md. Sakib Abrar Hossain , Kanchon Kanti Podder , Noha Barhom , Faleh Tamimi
show 2 more authors
Abdulrahman Alqahtani Muhammad E. H. Chowdhury
This is my paper · ORCID
classification cs.CVmath.OC
keywords cephalometriclandmarkdetectionSelf-ONNHRNetV2two-stageframeworkISBI2015datasetsuccessrateanatomicalclassificationorthodontictreatmentplanning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Self-CephaloNet is a two-stage deep learning framework for detecting 19 cephalometric landmarks in lateral skull X-rays, and the paper claims it reaches near the top of the ISBI 2015 challenge benchmark. In the first stage, a single network predicts all landmarks at once, achieving a 70.95% success rate within 2 mm on the Test1 and Test2 sets combined; the second stage refines each landmark on its own 512x512 patch, raising the success rate to 82.25%. The same second-stage model reaches 75.95% within 2 mm on an external cephalogram dataset [65] without retraining. The authors attribute the gain to replacing the standard bottleneck in an HRNetV2 backbone with a self-operational neural network block whose neurons learn their own nonlinear operators. If these results hold, orthodontists would have an end-to-end tool that automates landmarking at acceptable precision while reducing the need for multiple independently trained CNNs.

What carries the argument

The load-bearing component is the Self Bottleneck, a replacement for the standard convolutional bottleneck in the HRNetV2 backbone. It is built from Self-ONN layers, where each neuron's operator is a $q$-th order Taylor polynomial $\sum_{k=0}^{q} w_k x^k$; the weights $w_k$ are learned by backpropagation, so the network adapts the nonlinear operation per connection instead of using a fixed linear convolution. The framework also relies on a two-stage cascade: whole-image heatmap regression in stage one, then patch-based refinement with 19 per-landmark models in stage two. The paper attributes the accuracy gain to the adaptive nodal operators of Self-ONN, rather than to the high-resolution backbone alone.

What would settle it

Retrain the identical two-stage pipeline with the self-bottleneck replaced by a conventional convolutional bottleneck of the same width and parameter count, evaluating on an untouched test split rather than the cross-validation scheme; if the 2-mm success rate falls well below the reported 82.25% or the gap over plain HRNet disappears, the adaptive-node mechanism is not doing the claimed work.

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Extended reading notes

Core claim

The central claim is that a two-stage cascade built on HRNetV2 with a novel 'self-bottleneck' based on Self-ONN (self-operational neural networks) matches or exceeds prior state-of-the-art on the ISBI 2015 cephalometric landmark detection benchmark. Stage one predicts heatmaps for all 19 landmarks simultaneously from the whole image, giving a mean radial error of 1.53 mm on Test1 and 1.80 mm on Test2 and a combined 2-mm success detection rate of 70.95%. Stage two crops a 512x512 patch around each first-stage prediction and runs 19 per-landmark refinement models, improving the combined 2-mm SDR to 82.25% and the mean radial error to 1.08 mm on Test1 and 1.44 mm on Test2. On the external dataset [65], the stage-two model reports a 75.95% SDR at 2 mm, outperforming the cascaded CNN baseline [16] used for comparison. The paper further claims that the end-to-end first stage alone beats several published second-stage results, so the self-bottleneck's adaptive nodes are carrying real weight.

Load-bearing premise

The reported accuracy stands on the assumption that using Test2 as the validation set while reporting Test1 results (and Test1 as validation for Test2) did not bias model selection, so the Test1 and Test2 scores are genuine out-of-sample results.

Editorial extensions

If this is right

  • A single end-to-end first-stage network reaches a 70.95% 2-mm SDR across Test1 and Test2, so landmark detection does not require a multi-network cascade to be useful.
  • Adding per-landmark patch refinement lifts the 2-mm SDR to 82.25%, confirming that targeted local refinement remains a reliable source of accuracy in heatmap-based landmark localization.
  • The 75.95% 2-mm SDR on the external dataset [65] suggests the model transfers across scanner types and populations without additional training.
  • The reported anatomical classification averages (86.75% on Test1, 83.87% on Test2) imply that skeletal classification computed from predicted landmarks retains usable accuracy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A controlled ablation replacing only the Self-ONN layers with standard convolutions of the same width and parameter count would isolate how much of the gain comes from adaptive nodal operators; the comparison to plain HRNet in Table 7 suggests the attributed difference but does not control for training schedule.
  • Because Test2 served as the validation set while reporting Test1 (and Test1 for Test2), checkpoint selection may have leaked information; a re-evaluation on an untouched split, or using the external set [65] as the primary evidence, would give a cleaner measure of generalisation.
  • The landmark-to-measurement definitions in Table 1 appear to mix up anatomical points such as SNA and SNB angles; practitioners should verify those mappings before using the clinical classification numbers.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 8 minor

Summary. The paper proposes Self-CephaloNet, a two-stage deep learning framework for cephalometric landmark detection on lateral cephalograms. Stage 1 uses an HRNetV2 backbone with a novel Self-ONN bottleneck to predict 19 landmarks jointly; stage 2 trains 19 separate patch-based refinement models. The authors report a stage-1 success detection rate (SDR) of 70.94% and a stage-2 SDR of 82.25% within 2 mm on the ISBI 2015 Test1 and Test2 datasets, plus 75.95% on the external PKU dataset, together with anatomical classification accuracy. The central claim is that the architecture achieves state-of-the-art or near-state-of-the-art performance on this benchmark.

Significance. If the reported numbers were obtained under a clean evaluation protocol, the work would be of interest because it combines a high-resolution backbone with a Self-ONN bottleneck and demonstrates strong performance on a public benchmark, including external validation. The paper includes per-landmark error tables, multiple tolerance thresholds, a complexity analysis, and interpretability visualizations. The main limitation is that the reported test numbers are not independent evaluations because of the validation protocol in Section 5.4; the anatomical classification experiments are also compromised by incorrect definitions in Table 1.

major comments (4)
  1. [Section 5.4] The stated protocol invalidates the reported Test1 and Test2 scores as independent test evaluations. The text says: "During the evaluation of the Test1 dataset, we utilized the Test2 dataset as a validation set, while for evaluating the Test2 dataset, we employed the Test1 dataset as the validation set." Because validation curves typically guide early stopping, learning-rate schedule, and checkpoint selection, the model evaluated on Test1 is selected using Test2 and vice versa. The headline stage-2 average of 82.25% (Table 6) is only 0.17 percentage points above Oh et al. (82.08%), so the claimed superiority is well within the range of validation-selection noise. The authors should re-run the experiments with a proper held-out validation split (for example, a subset of the 150 training images) and report Test1 and Test2 results only after that split is fixed.
  2. [Table 1 and Section 5.2] The definitions of the eight clinical measurements in Table 1 are anatomically incorrect. For example, ANB is the angle between A-point, Nasion, and B-point, yet the table describes it as the angle between Landmark 5, Landmark 2, and Landmark 6, which are dental landmarks. Similar errors affect SNB, SNA, and other measures. Because Section 5.2 and Tables 8-11 report classification success rates computed from these measurements using the predicted landmark positions, those results cannot be interpreted as standard cephalometric classification and are not comparable to the cited methods. The authors should correct the definitions and recompute the classification evaluation.
  3. [Abstract and Section 5.1] The statement that "Our first-stage results surpassed previous studies" is contradicted by the paper's own Table 4, in which SCN Payer et al. (73.33%) outperforms the proposed stage-1 (70.94%) at the 2.0 mm threshold. Similarly, Table 6 shows that Oh et al. achieve higher SDR at 3.0 mm and 4.0 mm (92.34% and 96.92%) than the proposed method (92.00% and 96.25%). The claims of superiority should be limited to the specific metrics and thresholds where the comparison is actually favorable.
  4. [Section 3.2 and Table 7] The causal claim that the Self-ONN bottleneck is responsible for the improvement over HRNet is not established. Table 7 compares Self-CepahloNet with an HRNet baseline, but no details are given about the baseline's training schedule, data augmentation, patch size, or second-stage setup. A controlled ablation under identical training conditions, replacing only the Self-ONN bottleneck with a standard bottleneck, is required to attribute the performance difference to the Self-ONN component. Without such an ablation, the novelty claim is not supported.
minor comments (8)
  1. [Title/Abstract] The name is spelled "Self-CepahloNet" in the title and abstract but "Self-CephaloNet" elsewhere; please standardize the spelling throughout.
  2. [Section 5.7] The text refers to "Align-Net" without defining it, which appears to be an unintended reference or typo; please replace it with the correct model name or define the term.
  3. [Section 5.7 and Table 14] The text states "Mean Error (MRE) of 2.79 ± 1.87" but Table 14 reports "1.87 ± 2.79"; the order of MRE and SD is inconsistent and should be corrected.
  4. [Section 4.1] The term "Radical Error" should be "Radial Error" throughout the paper and in Figure 5.
  5. [Table 4] The table caption says "compared across the first and second stages of some method"; this is unclear and should be rewritten to indicate that the comparison is between the proposed first-stage results and other methods' final-stage results.
  6. [Section 5.6] The text uses "Gram Cam" and "Grad-CAM" interchangeably; please use the standard "Grad-CAM" consistently.
  7. [Section 4.4] The learning-rate schedule lists a reduction to 0.000001 at epoch 30 and again at epoch 50; if the 40th-epoch value is different, it should be stated explicitly.
  8. [Section 5.5 and 5.7] The abbreviation IPE is used inconsistently: "Image-specific Radical Error" in Section 5.5 and "Inverse Perspective Error" in Section 5.7; please define and use one term consistently.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported SDR numbers are empirical benchmark evaluations rather than derived predictions, so there is no definitional or fitted-input circularity; the main caveats are statistical-independence and completeness issues.

full rationale

The paper's central claims are measured SDR/MRE values on the ISBI 2015 Test1, Test2, and external PKU datasets. These are empirical benchmark results rather than quantities derived from assumptions, so there is no equation-level circularity: the reported 82.25% in Table 6 is obtained by counting landmarks whose radial error is below a threshold, not by construction from training labels or from the Self-ONN definition. The Self-ONN Taylor expansion in Eqs. 1-3 is imported from the external Self-ONN literature [21] and is used as an architectural component, not as a proof of the benchmark numbers. The main caveats are not circularity: Section 5.4 states that Test2 was used as the validation set when evaluating Test1 and vice versa, which threatens the statistical independence of the two test evaluations and could make the claimed advantage over Oh et al. (82.25 vs 82.08) fragile; this is an evaluation-protocol and overfitting concern, not a self-definitional reduction. Similarly, Table 1 mislabels several cephalometric measurements, which undermines the Section 5.2 classification comparisons, but that is a correctness error rather than a circular step. The absence of a same-training ablation isolating Self-ONN versus plain HRNetV2 means the architectural contribution is not independently established, but a missing baseline is not circularity. Self-citations to prior Self-ONN applications are not load-bearing for the empirical detection results, and no uniqueness theorem or fitted parameter is renamed as a prediction. Hence, no circular step is exhibited.

Assumptions & free parameters 10 free parameters · 5 assumptions · 0 invented entities

All benchmark numbers are empirical fits on a 150-image training set; the central claim depends on hand-set hyperparameters and on the test-as-validation protocol. No new physical or anatomical entities are introduced.

free parameters (10)
  • Image size = 256 x 256
    Hand-set input resolution; no reported ablation.
  • Heatmap size = 64 x 64
    Hand-set output resolution for landmark heatmaps.
  • Number of channels = 18, 36
    Hand-set channel widths for the HRNet-style stages.
  • Batch size per GPU = 16
    Chosen for training on a single RTX 3080 GPU.
  • Optimizer = Adam
    Chosen without reported comparison to other optimizers.
  • Learning rate schedule = 0.0001, stepped to 1e-5 at epoch 20 and 1e-6 at epochs 30 and 50
    Hand-set schedule; no reported ablation.
  • Sigma = 1.5
    Controls Gaussian heatmap spread; no reported ablation.
  • Epochs = 60
    Training length chosen by hand.
  • Self-ONN Taylor order q = 3
    Order of the Taylor approximation in Self-ONN layers; no ablation of q.
  • Stage-2 patch size = 512 x 512
    Patch size for the per-landmark refinement stage; no reported ablation.
assumptions (5)
  • standard math A truncated Taylor expansion of order q=3 is a sufficient approximation of the nonlinear neuron operator in Self-ONN (Eq. 3).
    The paper builds its Self Bottleneck on Self-ONN and relies on this expansion from Kiranyaz et al. without proof or ablation of q.
  • domain assumption The average of two expert landmark annotations is the correct ground truth.
    Stated in Section 3.1; used for all training and metric computation.
  • domain assumption Landmarks 1 and 2 define the inter-ocular distance used for NME normalization.
    Section 5.4 selects these points because they gave the most accurate NME, not for anatomical reasons; affects reported NME.
  • ad hoc to paper Using Test2 as validation for Test1 and vice versa does not bias reported performance.
    Section 5.4 states this protocol; if false, the reported SDR values are optimistic.
  • domain assumption Table 1's landmark-to-measurement mapping is anatomically correct.
    The table appears inconsistent with standard cephalometric definitions; if wrong, classification results are uninterpretable.

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Cite this review

Pith. "Pith review of Self-CephaloNet: A Two-stage Novel Framework using Operational Neural Network for Cephalometric Analysis." pith.science (2026). https://pith.science/paper/6B3IIURV

@misc{pith2026250110984,
  author       = {Pith},
  title        = {Pith review of: Self-CephaloNet: A Two-stage Novel Framework using Operational Neural Network for Cephalometric Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6B3IIURV}},
  note         = {Machine review of arXiv:2501.10984}
}
read the original abstract

Cephalometric analysis is essential for the diagnosis and treatment planning of orthodontics. In lateral cephalograms, however, the manual detection of anatomical landmarks is a time-consuming procedure. Deep learning solutions hold the potential to address the time constraints associated with certain tasks; however, concerns regarding their performance have been observed. To address this critical issue, we proposed an end-to-end cascaded deep learning framework (Self-CepahloNet) for the task, which demonstrated benchmark performance over the ISBI 2015 dataset in predicting 19 dental landmarks. Due to their adaptive nodal capabilities, Self-ONN (self-operational neural networks) demonstrate superior learning performance for complex feature spaces over conventional convolutional neural networks. To leverage this attribute, we introduced a novel self-bottleneck in the HRNetV2 (High Resolution Network) backbone, which has exhibited benchmark performance on the ISBI 2015 dataset for the dental landmark detection task. Our first-stage results surpassed previous studies, showcasing the efficacy of our singular end-to-end deep learning model, which achieved a remarkable 70.95% success rate in detecting cephalometric landmarks within a 2mm range for the Test1 and Test2 datasets. Moreover, the second stage significantly improved overall performance, yielding an impressive 82.25% average success rate for the datasets above within the same 2mm distance. Furthermore, external validation was conducted using the PKU cephalogram dataset. Our model demonstrated a commendable success rate of 75.95% within the 2mm range.

Figures

Figures reproduced from arXiv: 2501.10984 by the authors.

Figure 3
Figure 3. The overall framework of the Self-CepahloNet model. The proposed algorithm is divided into two parts, Initial portion (1st stage) for identifying the area of interest and landmark prediction (2nd stage) to determine the precise location of landmarks, also known as the refinement stage. The overall framework of the Self-CepahloNet model is illustrated in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 10
Figure 10. Grad-CAM Visualization Highlighting Model Interpretability and Confidence Regions. To better understand model interpretability, we trained the SelfCephaloNet model on five cases, as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗

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