REVIEW 2 major objections 4 minor 1 cited by
TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation
T0 review · 2 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash
Pith's one-line read TP-UNet claims that feeding a UNet a text prompt naming the imaging modality, the organ, and its normalized position in the slice stack yields state-of-the-art segmentation on two medical benchmarks.
desk verdict A plausible temporal-prompt segmentation idea with strong reported numbers, but the key ablation removes the organ name along with the timestamp, so the central claim is not yet isolated. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing mechanism is the temporal prompt template, where the normalized slice index $N_i/N$ turns the ordering of a scan into a token the text encoder can represent. Around that prompt, two learned components carry the argument: the semantic align module, a bidirectional contrastive loss that pulls matching prompt-image pairs together and pushes non-matching pairs apart, and the modality fusion block, a cross-attention over concatenated projected text and image features whose output is concatenated with the UNet's first-level skip connection before decoding. Together they implement the prior that each organ appears with roughly a normal probability over the scan's temporal interval.
What would settle it
Train the full TP-UNet with the same prompts but replace each timestamp $N_i/N$ with a random value in $[0,1]$, keeping organ names and modalities unchanged. If average Dice on UW-Madison and liver Dice on LiTS stay near 0.9286 and 0.9125, the temporal fraction carries no learnable signal and the paper's central claim would be refuted.
Extended reading notes
Core claim
The paper's central claim is that temporal prompts—short texts of the form "This is {an MRI / a CT} of the {organ} with a segmentation period of {N_i/N}"—guide a standard UNet to state-of-the-art segmentation. The temporal signal has real content because organ occurrence over the normalized scan position roughly follows a normal distribution: stomach peaks early, small intestine in the middle, large intestine late, and liver near 0.78 in CT. TP-UNet encodes the prompt with a fine-tuned text encoder (CLIP with LoRA or Electra with SFT), aligns the text and image embedding spaces with an unsupervised contrastive loss, and fuses them through a cross-attention block whose output joins the UNet decoder's first skip connection. Reported numbers beat the strongest compared baseline, Swin UNet, by 1.3% average Dice on UW-Madison and 9.21% liver Dice on LiTS; ablations attribute 2.1% on UW-Madison to the timestamp and 5.36% on LiTS to the full prompt.
Load-bearing premise
The method's claimed advantage rests on the premise that the normalized slice position in the prompt, rather than the organ name, is the cue responsible for the reported gains; the ablations vary the timestamp or remove the whole prompt but never vary the organ name alone while holding the timestamp fixed.
Editorial extensions
If this is right
- On UW-Madison, replacing the prompt's timestamp with no timestamp lowers average Dice by 2.1%, so the temporal fraction itself contributes to the reported result.
- On LiTS 2017, removing the temporal prompt entirely lowers liver Dice by 5.36%, showing the prompt as a whole is a sizable part of the gain.
- Removing the semantic-alignment contrastive loss costs 1.01% average Dice on UW-Madison, so aligning modalities before fusing them matters.
- Both text encoders work: CLIP with LoRA reaches 0.9286 average Dice on UW-Madison, and Electra with SFT reaches 0.9125 liver Dice on LiTS.
Reading between the lines
- Beyond the paper: a direct ablation that swaps the timestamp for a random value while keeping organ names fixed would settle whether the temporal fraction or the organ-localization cue drives the gains.
- Beyond the paper: the same prompt mechanism could be tested on other ordered imaging, such as cardiac cine series or pathology z-stacks, where structure occurrence follows a known sequence.
- Beyond the paper: because the contrastive alignment is unsupervised, the framework could be pretrained on unlabeled paired scans and prompts, then adapted to new organs with fewer annotations.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes TP-UNet, a UNet-based medical image segmentation framework that injects temporal information through textual prompts of the form "This is an MRI / a CT of the {organ} with a segmentation period of {N_i/N}." The text prompt is encoded with CLIP (LoRA-tuned) or Electra (SFT-tuned), aligned to image features via an unsupervised contrastive loss, and fused through cross-attention with UNet encoder features before decoding. Experiments on the UW-Madison gastrointestinal MRI dataset and the LiTS 2017 liver CT dataset report improved Dice/Jaccard scores over several UNet variants, with the best configuration reaching 0.9286 average Dice on UW-Madison and 0.9125 liver Dice on LiTS. Ablations remove the temporal information, the whole prompt, the semantic alignment, and the modality fusion. The authors claim state-of-the-art performance and plan to release the code after acceptance.
Significance. If the reported gains are attributable to temporal information, the work would be a useful and simple contribution: it formulates slice position as a continuous prompt, aligns text and image modalities with contrastive learning, and fuses the prompt through cross-attention. The automatic prompt generation (under 1 ms per slice) and the use of parameter-efficient text encoders are practical strengths, and the paper is commendably explicit about its template. However, the central claim that temporal information drives the improvement is not established by the current experiments because the temporal ablation is confounded with removal of the organ name, and the baseline comparisons omit sequence-aware and prompt-based methods. The LiTS preprocessing is also under-specified. These issues are fixable with additional experiments, so the manuscript has potential, but the current evidence is not sufficient to support the headline conclusion.
major comments (2)
- [IV-A.1] The LiTS preprocessing is under-specified: the paper states that 58,638 2D slices were divided but "a large number of 2D slices are also redundant" and finally 10,967 slices were selected, without giving the selection criterion. If the retained slices are chosen based on organ presence, image quality, or another property correlated with difficulty, the comparison against baselines trained on the same subset could be biased. Please state the exact filtering rule and any exclusion statistics, and confirm that all compared methods use the same filtered slices with the same train/validation/test split.
- [II-A and IV-B] The comparison omits the most relevant sequence-aware and prompt-based methods. The paper cites SIA-UNet [3] as a sequence-aware UNet variant but does not include it in Tables I or II, and it cites prompt-based segmentation works [5], [6] but does not compare against them. Without these baselines, the claim of "state-of-the-art performance" is not supported. Please add at least one sequence-aware baseline (SIA-UNet or a temporal-unaware variant) and one prompt-based segmentation baseline, or justify their exclusion with a concrete explanation.
minor comments (4)
- [IV-B, last paragraph] The LiTS paragraph states "the most significant improvement of 9.47% in the Small Intestine category," but LiTS in Table II only reports liver segmentation; this sentence appears to be carried over from the UW-Madison discussion and should be corrected.
- [IV-D, Table III caption] The caption says "a lower score indicates a greater contribution of the module to the TP-UNet model," but the table reports Dice and Jaccard where higher is better; please rephrase the caption to state that removing a module decreases the score.
- [III-A] The notation {N_i/N} is used without defining N_i; if N is the total number of slices in a scan, N_i should be explicitly defined as the slice index (or normalized position) so that the timestamp lies in [0,1].
- [III-A and IV-A] The phrase "with a segmentation period of {N_i/N}" is awkward; consider "at normalized slice position {N_i/N}" or "at timestamp {N_i/N}" for clarity. Also, the implementation details mention initial temperature 25 and maximum 96.875 but do not explain how the temperature schedule in Eq. (1) is updated; please clarify.
Circularity Check
No circularity: TP-UNet's derivation and ablations are self-contained; the closest issue is an ablation confound, not a circular reduction.
full rationale
The paper's central design—the temporal prompt template (Section III-A), the semantic-alignment contrastive losses (Eqs. 1–4), and the cross-attention fusion (Eq. 5)—is original and does not define any predicted quantity in terms of the quantity being predicted. The state-of-the-art claims are evaluated on held-out UW-Madison and LiTS test splits against external baselines (Tables I–II), and the losses and Dice/Jaccard metrics are standard. The only self-citation, [3] (SIA-UNet, whose author list includes R. Wang), is used to support the ordinary observation that existing UNet variants neglect temporal sequence information; it is not load-bearing for TP-UNet's mechanism or results. The ablation labeled 'w/o Temporal Information' (Table III) replaces the prompt with 'This is an MRI / a CT of the organ', which removes both the organ name and the timestamp, so the 2.1% and 3.9% drops do not isolate the temporal contribution; however, that is an experimental confound rather than a circular derivation—no equation or claim is assumed equivalent to its own input. Hence no enumerated circularity pattern applies.
Assumptions & free parameters
free parameters (2)
- temperature tau =
initial 25, maximum 96.875
- contrastive weight lambda =
not reported
assumptions (3)
- domain assumption Organ occurrence along the slice axis approximately follows a normal distribution with a fixed ordering of means (stomach, small intestine, large intestine).
- domain assumption The slice fraction N_i/N is a meaningful temporal coordinate shared across patients and scans.
- standard math InfoNCE contrastive loss aligns cross-modal features in a shared space.
Cite this review
Pith. "Pith review of TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation." pith.science (2026). https://pith.science/paper/TYY5CZJL
@misc{pith2026241111305,
author = {Pith},
title = {Pith review of: TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation},
year = {2026},
howpublished = {\url{https://pith.science/paper/TYY5CZJL}},
note = {Machine review of arXiv:2411.11305}
}
read the original abstract
The advancement of medical image segmentation techniques has been propelled by the adoption of deep learning techniques, particularly UNet-based approaches, which exploit semantic information to improve the accuracy of segmentations. However, the order of organs in scanned images has been disregarded by current medical image segmentation approaches based on UNet. Furthermore, the inherent network structure of UNet does not provide direct capabilities for integrating temporal information. To efficiently integrate temporal information, we propose TP-UNet that utilizes temporal prompts, encompassing organ-construction relationships, to guide the segmentation UNet model. Specifically, our framework is featured with cross-attention and semantic alignment based on unsupervised contrastive learning to combine temporal prompts and image features effectively. Extensive evaluations on two medical image segmentation datasets demonstrate the state-of-the-art performance of TP-UNet. Our implementation will be open-sourced after acceptance.
Figures
Forward citations
Cited by 1 Pith paper
-
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey
A broad survey that organizes prompt mechanisms for medical image generation, segmentation, and classification into a two-dimensional taxonomy of core technologies and clinical applications.
Reference graph
Works this paper leans on
-
[3]
Sia-unet: A unet with s equence information for gastrointestinal tract segmentation,
R. Y e, R. Wang, Y . Guo, and L. Chen, “Sia-unet: A unet with s equence information for gastrointestinal tract segmentation,” in Pacific Rim International Conference on Artificial Intelligence . Springer, 2022, pp. 316–326
work page 2022
-
[5]
Clip-driven universal mode l for organ segmentation and tumor detection,
J. Liu, Y . Zhang, J.-N. Chen, J. Xiao, Y . Lu, B. A. Landman, Y . Y uan, A. Y uille, Y . Tang, and Z. Zhou, “Clip-driven universal mode l for organ segmentation and tumor detection,” arXiv preprint arXiv:2301.00785 , 2023
arXiv 2023
-
[6]
Medical sam adapter: Adapting segment anything model for m edical image segmentation,
J. Wu, R. Fu, H. Fang, Y . Liu, Z. Wang, Y . Xu, Y . Jin, and T. Ar bel, “Medical sam adapter: Adapting segment anything model for m edical image segmentation,” arXiv preprint arXiv:2304.12620 , 2023
arXiv 2023
-
[1]
M. Khened, V . A. Kollerathu, and G. Krishnamurthi, “Full y convo- lutional multi-scale residual densenets for cardiac segme ntation and automated cardiac diagnosis using ensemble of classifiers, ” Medical Image Analysis , 2018
work page 2018
-
[2]
U-net and its variants for medical image segmentation: A review of the ory and applications,
N. Siddique, S. Paheding, C. P . Elkin, and V . Devabhaktun i, “U-net and its variants for medical image segmentation: A review of the ory and applications,” IEEE Access , vol. 9, pp. 82 031–82 057, 2021
work page 2021
-
[4]
Align before fuse: Vision and language representatio n learning with momentum distillation,
J. Li, R. Selvaraju, A. Gotmare, S. Joty, C. Xiong, and S. C . H. Hoi, “Align before fuse: Vision and language representatio n learning with momentum distillation,” Advances in neural information processing systems, vol. 34, pp. 9694–9705, 2021
work page 2021
-
[7]
Contrastive learning of medical visual representations f rom paired images and text,
Y . Zhang, H. Jiang, Y . Miura, C. D. Manning, and C. P . Langl otz, “Contrastive learning of medical visual representations f rom paired images and text,” in Machine Learning for Healthcare Conference . PMLR, 2022, pp. 2–25
work page 2022
-
[8]
S.-C. Huang, L. Shen, M. P . Lungren, and S. Y eung, “Gloria : A multimodal global-local representation learning framewo rk for label- efficient medical image recognition,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3942–3951
work page 2021
Show all 26 references
-
[9]
Learning transferable visual models from natural language supervision,
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Aga rwal, G. Sastry, A. Askell, P . Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
-
[10]
Parameter-efficient fine-t uning of large-scale pre-trained language models
N. Ding, Y . Qin, G. Y ang, F. Wei, Z. Y ang, Y . Su, S. Hu, Y . Chen, C.-M. Chan, W. Chen, J. Yi, W. Zhao, X. Wang, Z. Liu, H. Zheng, J. Chen , Y . Liu, J. Tang, J. Li, and M. Sun, “Parameter-efficient fine-t uning of large-scale pre-trained language models.”
-
[11]
Lora: Low-rank adaptation of large language mo dels,
E. J. Hu, Y . Shen, P . Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language mo dels,” arXiv preprint arXiv:2106.09685 , 2021
2021 arXiv
-
[12]
Elect ra: Pre- training text encoders as discriminators rather than gener ators,
K. Clark, M.-T. Luong, Q. V . Le, and C. D. Manning, “Elect ra: Pre- training text encoders as discriminators rather than gener ators,” arXiv preprint arXiv:2003.10555, 2020
2003 arXiv
-
[13]
Training language models to follow instructions with human feedback,
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P . Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in neural information processing systems , vol. 35, pp. 27 730–27 744, 2022
2022
-
[14]
U-net: Convol utional networks for biomedical image segmentation,
O. Ronneberger, P . Fischer, and T. Brox, “U-net: Convol utional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th Int ernational Conference, Munich, Germany, October 5-9, 2015, Proceedin gs, Part III
2015
-
[15]
V ery deep convolutional networks for large-scale image recognition,
K. Simonyan and A. Zisserman, “V ery deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014
2014 arXiv
-
[16]
Deep residual learni ng for image recognition,
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learni ng for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
-
[17]
Unet++: Redesigning skip connections to exploit multiscale featur es in image segmentation,
Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “ Unet++: Redesigning skip connections to exploit multiscale featur es in image segmentation,” IEEE transactions on medical imaging , vol. 39, no. 6, pp. 1856–1867, 2019
2019
-
[18]
Springer, 2015, pp. 234–241
2015
-
[19]
Atten- tion u-net: Learning where to look for the pancreas,
O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich , K. Misawa, K. Mori, S. McDonagh, N. Y . Hammerla, B. Kainz et al. , “Atten- tion u-net: Learning where to look for the pancreas,” arXiv preprint arXiv:1804.03999, 2018
2018 arXiv
-
[20]
Concurrent spati al and channel ‘squeeze & excitation’in fully convolutional networks,
A. G. Roy, N. Navab, and C. Wachinger, “Concurrent spati al and channel ‘squeeze & excitation’in fully convolutional networks,” i n Medical Image Computing and Computer Assisted Intervention–MICCA I 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Pr...
2018
-
[21]
Transunet: Transformers make strong encoders for medical image segmentation,
J. Chen, Y . Lu, Q. Y u, X. Luo, E. Adeli, Y . Wang, L. Lu, A. L. Y uille, and Y . Zhou, “Transunet: Transformers make strong encoders for medical image segmentation,” arXiv preprint arXiv:2102.04306 , 2021
2021 arXiv
-
[22]
An image is worth 16x16 words: Transformers for image recog nition at scale,
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenbor n, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gel ly et al. , “An image is worth 16x16 words: Transformers for image recog nition at scale,” arXiv preprint arXiv:2010.11929 , 2020
2010 arXiv
-
[23]
Swin-unet: Unet-like pure transformer for medical image s egmenta- tion,
H. Cao, Y . Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M . Wang, “Swin-unet: Unet-like pure transformer for medical image s egmenta- tion,” in Computer Vision–ECCV 2022 W orkshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part III . Springer, 2023, pp. 205– 218
2022
-
[24]
Swin transformer: Hierarchical vision transforme r using shifted windows,
Z. Liu, Y . Lin, Y . Cao, H. Hu, Y . Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transforme r using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 012–10 022
2021
-
[25]
Uw-madison gi tract image segmentation,
happyharrycn, Maggie, P . Culliton, P . Y adav, and S. L. L ee, “Uw-madison gi tract image segmentation,” 2022. [Online]. Available: https://kaggle.com/competitions/uw-madison-gi-tract-image-segmentation
2022
-
[26]
The liver tumor segmentation benchmark (lits),
P . Bilic, P . Christ, H. B. Li, E. V orontsov, A. Ben-Cohen , G. Kaissis, A. Szeskin, C. Jacobs, G. E. H. Mamani, G. Chartrand et al., “The liver tumor segmentation benchmark (lits),” Medical Image Analysis , vol. 84, p. 102680, 2023
2023
Reviewed August 12, 2026 · model on record in the stance chip above.
Discussion (0). Continue with ORCID to comment.