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REVIEW 5 major objections 6 minor 32 references

TCM-Tongue: A Standardized Tongue Image Dataset with Pathological Annotations for AI-Assisted TCM Diagnosis

T0 review · 5 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper introduces a dataset of 6,719 standardized tongue images with 20 expert symptom labels, and benchmarks nine object-detection models on it, aiming to give AI-assisted TCM diagnosis a common, publicly available testbed.

desk verdict Useful new tongue dataset with sensible labels, but the 'clinically validated' claim lacks the reliability evidence the benchmark numbers depend on. read the letter →

arxiv 2507.18288 v1 pith:DXK2BYKX submitted 2025-07-24 eess.IV cs.AIcs.CV

Xuebo Jin , Longfei Gao , Anshuo Tong , Zhengyang Chen , Jianlei Kong , Ning Sun , Huijun Ma , Qiang Wang
show 2 more authors
Yuting Bai Tingli Su
This is my paper · ORCID
classification eess.IVcs.AIcs.CV
keywords tonguediagnosisdatasettraditionalChinesemedicineobjectdetectionbenchmarkmulti-labelannotationstandardizedimagingprotocoldeeplearningmedicalsymptomclassification
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

This paper introduces TCM-Tongue, a dataset of 6,719 tongue photographs taken under a standardized capture protocol and labeled by TCM practitioners with 20 symptom categories, averaging 2.54 labels per image. The authors position it as the first publicly available, AI-ready dataset for TCM tongue diagnosis, filling a gap that has blocked deep learning work on tongue examination. The dataset ships in COCO, XML, and TXT annotation formats, and the paper benchmarks nine object-detection models to show what current architectures achieve on it. If the dataset and its labels hold up, it gives the field a common testbed for automating a diagnostic tradition that has until now been subjective and hard to reproduce.

What carries the argument

The central object is the annotation framework: a dual-level label system in which global labels describe the whole tongue (for example red tongue, white coating, thin tongue) and local labels describe subregions (cracks, teeth marks, depressed or protruding organ areas), all encoded as bounding boxes with class indices 0–19. The other load-bearing component is the standardized acquisition protocol: a purpose-built dual-camera capture system with calibrated D65 lighting, distance-controlled wide-angle and telephoto imaging, and automatic quality checks, designed so that all images share similar geometry and color. Together these convert an observational, subjective clinical skill into a quantifiable object-detection task.

What would settle it

Select 300 images at random from the released set, have two independent licensed TCM practitioners label each image with the same 20 categories, and compute per-category Cohen's kappa. If several categories fall below roughly 0.6, the labels are too inconsistent to count as ground truth, and the reported detector rankings lose their anchor.

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Extended reading notes

Core claim

The central claim is that expert-reviewed, standardized collection can convert TCM tongue diagnosis into a computable object-detection problem. The dataset contains 6,719 images annotated with 20 symptom categories, split into global and local features, averaging 2.54 labels per image, with labels reviewed by licensed practitioners. The authors benchmark nine detection models, report mAP@0.5 up to about 35%, and interpret the results as showing that the dataset is usable and that mid-sized detectors offer the best accuracy/compute trade-off. These results are meant to establish the dataset as a foundation for further AI-assisted TCM research.

Load-bearing premise

The central assumption is that the expert-reviewed labels are correct, consistent, and complete enough to serve as clinical ground truth, but the paper does not report any inter-annotator agreement or reliability statistics.

Editorial extensions

If this is right

  • With a common dataset and label taxonomy, results of different tongue-diagnosis models become directly comparable.
  • The multi-label structure lets a single image express coexisting signs, so models can be trained to output combination patterns rather than a single disease label.
  • The reported benchmarks give a numerical starting point (best mAP@0.5 around 35%) that future work can try to beat.
  • The standardized acquisition protocol makes dataset extension reproducible, so the collection can grow while preserving image conditions.

Reading between the lines

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

  • A published per-label inter-annotator agreement study would let downstream users weight labels by their reliability and would likely be a prerequisite for clinical deployment.
  • The dataset invites a domain-shift experiment: train on the standardized images, then evaluate on smartphone-captured or clinic-casual tongue photos to see how far standardization transfers.
  • A natural follow-up is linking these 20 visual labels to TCM syndrome patterns or to modern laboratory measures, making the dataset a bridge between visual features and clinical endpoints.
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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

5 major / 6 minor

Summary. The manuscript presents TCM-Tongue, a dataset of 6,719 tongue images with multi-label object-detection annotations for 20 TCM symptom categories, captured with a custom standardized acquisition system and reviewed by TCM practitioners. The authors describe the hardware, the 20-class taxonomy, dataset statistics (an average of 2.54 labels per image), train/validation/test splits, and benchmark results for nine detectors (YOLOv5/7/8 variants, SSD, MobileNetV2), reporting precision, recall, mAP@0.5, and mAP@0.5:0.95. The dataset, multi-format annotations, and code are publicly available. The central claims are that this is the first publicly available, standardized, clinically validated TCM tongue dataset suitable for AI development.

Significance. The resource addresses a real gap: publicly available, AI-ready TCM tongue datasets with standardized annotations are scarce, and the paper provides multi-format annotations, a structured taxonomy, example images, and baseline code. If the label-reliability and acquisition-standardization claims hold, this dataset would be a useful benchmark for the TCM-AI community and for studying fine-grained medical object detection. The paper is also informative in reporting the limitations of current detectors, such as red/purple tongue confusion and misses of cracked and dentate tongues. The primary value is the dataset itself rather than the detector comparison; the moderate mAP values are honestly reported and do not undermine the dataset contribution, provided the reliability evidence is added.

major comments (5)
  1. [Label Selection and Annotation] The 'clinically validated' ground truth, on which both the dataset's value and every benchmark number depend, is not empirically supported. The description of two-stage review by technicians and TCM practitioners is qualitative; no inter-annotator agreement statistic (e.g., Cohen's or Fleiss' kappa), number of annotators per image, blinded re-annotation study, or operational definition of 'clinically validated' is reported. Please add per-class annotation counts, an adjudication protocol, annotator qualification details, and a label-noise analysis on a re-annotated subset. Without this, the mAP scores in Table 2 cannot be interpreted as properties of the images rather than of one unrepeatable annotation process.
  2. [Methodological Implementation of the Tongue Diagnosis Capture System] The claimed standardization of image acquisition is asserted rather than validated. The text states D65 illumination, 500-1500 lux adjustment, sub-100 micrometer resolution, and 3-8 second cycles, but no calibration certificates, color-correction measurements, focus-target tests, inter-device agreement, or repeatability data are provided. Please add a validation protocol with quantitative results such as color-checker Delta-E values, resolution-chart measurements, and within-session and between-session repeatability, and clarify how the logged metadata allows per-image auditing of these parameters. The stated real-time ResNet-50 demographic profiling of subjects also raises privacy and bias concerns; please explain what data are stored, for how long, and how bias and consent are handled.
  3. [Technical Validation (Table 2; Eqs. (1)-(5))] The benchmark comparison is single-run and has no error bars or statistical testing. Differences such as mAP@0.5 values of 34.57 (YOLOv5l), 34.82 (YOLOv7), and 34.95 (YOLOv8l) are likely within seed and initialization variance, so the text's conclusions about which models are 'best' are not justified. Please report means and standard deviations over at least 3-5 seeds, per-class AP, and a significance analysis or critical-difference test. In addition, Equations (1)-(5) are not displayed in the manuscript, so the precise definitions of P, R, mAP0.5, and mAP0.5:0.95 are not actually provided.
  4. [Data Records and Fig. 1] Several details necessary for assessing a medical dataset are missing: institutional review board approval or an ethics statement, the consent process beyond a one-line mention, participant inclusion and exclusion criteria, demographic and diagnostic distribution, collection sites and date range, and image resolution and format. The paper also contains an internal inconsistency: Fig. 1 states an 80/10/10 split, while Section 4 reports 82.3/8.4/8.1; please reconcile or correct this. These details are needed for the 'clinically validated' and 'standardized' claims to be auditable.
  5. [Background & Summary (Related Image Datasets)] The 'first specialized dataset' claim is not supported by a baseline survey. The related-datasets paragraph lists datasets in other imaging domains but does not systematically compare existing TCM tongue datasets or publicly available tongue-image collections in terms of size, annotation type, illumination control, and availability. Please add a comparison table and restrict the 'first' claim to what can actually be verified from the cited prior work.
minor comments (6)
  1. [Technical Validation] The notation for the mAP variants is inconsistent (mAP0.5-0.95, mAP0.50.95, mAP@0.5); please standardize to mAP@0.5 and mAP@0.5:0.95.
  2. [Fig. 6 and Data Records] Figure 6 shows per-label counts, but the text only states the average number of labels per image; please provide the per-image label-count distribution and a per-class instance count table with the number of images containing each label.
  3. [References and Usage Notes] Reference [11] contains spacing errors in the title ('Tongue Di agnosi s i n Chi nese Medicine'), and the Usage Notes refers to 'SD[29]' where 'SSD' is meant.
  4. [Data Records] The exact image resolution, bit depth, color space, and storage format should be stated; 'high-resolution source images' is not a technical specification.
  5. [Fig. 1] Figure 1's caption lists three panel functions, but the subfigures are not individually labeled; please add panel labels and describe what each panel shows.
  6. [Technical Validation] The text refers to 'the first 100 rounds of training' in Figure 8; please specify whether these are epochs and report the training hyperparameters such as learning rate, batch size, and total number of epochs.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a dataset descriptor whose benchmark numbers and label statistics are measured from external annotations, not derived from its own outputs.

full rationale

This is a data descriptor rather than a derivation paper. The central quantities—6,719 images, 20 categories, 2.54 labels per image, and the mAP values in Table 2—are reported as measurements of an externally constructed resource. The label set is selected from classical TCM literature and clinical consensus (references [10][11], Huangdi Neijing and Maciocia), and annotations are produced by trained technicians and reviewed by TCM practitioners with more than five years of experience. No equation defines a predicted quantity in terms of the paper's own fitted parameters, and no uniqueness theorem or self-citation is invoked to force a conclusion. The benchmark performance figures are computed on a held-out test split using standard detection metrics, so they are not statistically forced by the training labels. The absence of inter-annotator agreement statistics is a legitimate concern about label reliability and the strength of the 'clinically validated' claim, but it is a validation gap, not circularity: an unreliable label does not make the reported numbers definitionally equal to the paper's inputs. Accordingly, no circular step can be quoted or exhibited, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper's contribution is a dataset, so it rests on domain assumptions about TCM label validity and annotation reliability, plus an unverified hardware specification. No free parameters are fitted to data.

assumptions (3)
  • domain assumption TCM tongue diagnosis categories (the 20 labels) are clinically meaningful and reliably identifiable from static tongue images.
    The dataset and all benchmark conclusions rest on this. The paper cites classical texts [10][11] but offers no empirical validation of the mapping from image features to diagnostic categories.
  • domain assumption Bounding-box object detection is an appropriate task formulation for TCM tongue diagnosis.
    The paper annotates global and local features with boxes and benchmarks YOLO and SSD detectors, assuming tongue features can be meaningfully localized by bounding boxes.
  • ad hoc to paper The custom capture system achieves the stated standardization (D65 lighting, 3-8 second cycles, sub-100 micrometer resolution) as described.
    No calibration data or hardware validation are provided in 'Methodological Implementation of the Tongue Diagnosis Capture System'.

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Cite this review

Pith. "Pith review of TCM-Tongue: A Standardized Tongue Image Dataset with Pathological Annotations for AI-Assisted TCM Diagnosis." pith.science (2026). https://pith.science/paper/DXK2BYKX

@misc{pith2026250718288,
  author       = {Pith},
  title        = {Pith review of: TCM-Tongue: A Standardized Tongue Image Dataset with Pathological Annotations for AI-Assisted TCM Diagnosis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DXK2BYKX}},
  note         = {Machine review of arXiv:2507.18288}
}
read the original abstract

Traditional Chinese medicine (TCM) tongue diagnosis, while clinically valuable, faces standardization challenges due to subjective interpretation and inconsistent imaging protocols, compounded by the lack of large-scale, annotated datasets for AI development. To address this gap, we present the first specialized dataset for AI-driven TCM tongue diagnosis, comprising 6,719 high-quality images captured under standardized conditions and annotated with 20 pathological symptom categories (averaging 2.54 clinically validated labels per image, all verified by licensed TCM practitioners). The dataset supports multiple annotation formats (COCO, TXT, XML) for broad usability and has been benchmarked using nine deep learning models (YOLOv5/v7/v8 variants, SSD, and MobileNetV2) to demonstrate its utility for AI development. This resource provides a critical foundation for advancing reliable computational tools in TCM, bridging the data shortage that has hindered progress in the field, and facilitating the integration of AI into both research and clinical practice through standardized, high-quality diagnostic data.

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