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REVIEW 4 major objections 5 minor 39 references

UNet++ and LSTM combined approach for Breast Ultrasound Image Segmentation

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A hybrid of UNet++ and LSTM with attention and multiscale feature modules reports 98.88% accuracy in segmenting breast ultrasound lesions on the BUSI dataset.

desk verdict A plausible architecture combo whose reported superiority over 15 baselines is not supported by the evaluation as presented; worth a revision, not rejection on idea alone. read the letter →

arxiv 2412.05585 v1 pith:QHZYEIKI submitted 2024-12-07 cs.CV cs.AIcs.CL

Saba Hesaraki , Morteza Akbari , Ramin Mousa This is my paper · ORCID
classification cs.CVcs.AIcs.CL
keywords breastultrasoundsegmentationUNet++LSTMself-attentionmulti-scalefeatureextractionBUSIdatasetmedicalimagedataaugmentation
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 proposes a breast ultrasound segmentation model that grafts LSTM-based attention and a multiscale feature extraction module onto the UNet++ encoder-decoder. The authors claim that by adding these components they can capture both spatial structure and temporal (recurrent) feature dependencies, which plain UNet++ ignores. With data augmentation on the public BUSI breast ultrasound dataset, the model reports accuracy 98.88%, specificity 99.53%, precision 95.34%, sensitivity 91.20%, F1-score 93.74%, and Dice 92.74%. The paper presents these numbers as surpassing all fifteen prior methods it compares against. A sympathetic reader would care because accurate tumor boundary segmentation on routine ultrasound is a practical step toward earlier breast cancer detection.

What carries the argument

The architecture, named UNet++LSTM, keeps UNet++'s nested encoder-decoder with dense convolution blocks on hopping paths and adds three components: a multiscale feature extraction module that produces feature maps at different scales; a Convolutional Block Attention Module (CBAM) whose channel branch routes average- and max-pooled descriptors through LSTM gates before combining them with element-wise summation; and a multi-task learning head that adds a distance-class map prediction term to the segmentation loss. The LSTM inside the channel attention is the load-bearing novelty: the paper describes it as temporal feature learning, and the attention weights determine how much each hierarchical feature map contributes to the final segmentation. The multiscale module and attention together are what the argument credits for the reported gains over plain UNet++.

What would settle it

Run the proposed model and every comparison method on the same patient-stratified split of BUSI with identical preprocessing, augmentation, and metric computation; if any baseline reaches or exceeds a Dice of 92.74 on that shared protocol, the paper's headline claim of superiority fails.

Watch

Extended reading notes

Core claim

The central claim is that replacing plain UNet++ with a version enriched by an attention block, a multiscale feature extraction module, and LSTM layers yields a segmentation pipeline that outperforms plain UNet++ and the other published models compared in the paper on breast ultrasound images. On the BUSI dataset with data augmentation, the proposed UNet++LSTM model reaches accuracy 98.88%, specificity 99.53%, precision 95.34%, sensitivity 91.20%, F1-score 93.74%, and Dice 92.74%, improving over UNet++ with data augmentation by raising Dice from 90.70 to 92.74 and F1 from 90.70 to 93.74. The paper argues these gains show the architecture is a competitive, end-to-end solution for extracting benign and malignant tumor regions.

Load-bearing premise

The comparison claim assumes the published metrics of the fifteen prior methods are directly comparable to the proposed model's single 80/20 split of the BUSI dataset, even though the cited papers often use different datasets, preprocessing, splits, and metric definitions.

Editorial extensions

If this is right

  • Adding LSTM-based attention and multiscale feature extraction to UNet++ increases Dice from 90.70 to 92.74 and F1 from 90.70 to 93.74 on the BUSI dataset with data augmentation.
  • The paper reports a 1.25 improvement in precision over the best comparative approach it lists.
  • The proposed model reports specificity 99.53%, surpassing the ODET model's 98.63% in the comparison table.
  • According to the loss and accuracy curves, both UNet++ and the proposed model train without overfitting or underfitting, which the paper offers as evidence of stable learning.
  • The architecture is end-to-end and trainable with standard data augmentation on the BUSI dataset, requiring no additional imaging modality.

Reading between the lines

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

  • A testable extension the paper does not run: if LSTM recurrence is the active ingredient, evaluating on consecutive ultrasound cine frames instead of single images should widen the margin over plain UNet++.
  • The comparison against literature values assumes protocol equivalence; a single-protocol reimplementation of the same baselines on the same BUSI split could yield a different ranking.
  • The same channel-attention-with-LSTM design could plausibly transfer to other boundary-delineation tasks such as lesion segmentation in CT or MRI, though the paper only reports ultrasound results.
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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 / 5 minor

Summary. The paper proposes UNet++LSTM, an encoder-decoder architecture for breast ultrasound image segmentation on the BUSI dataset. The method combines a multiscale feature extraction module, CBAM-style channel and spatial attention in which channel descriptors are processed by an LSTM, and a multi-task loss. The authors report accuracy 98.88%, specificity 99.53%, precision 95.34%, sensitivity 91.20%, F1-score 93.74%, and Dice 92.74% on a single 80/20 split with data augmentation, and they claim that the model outperforms all 15 compared literature methods. The paper includes a comparison table, loss/accuracy curves, sample segmentations, and implementation settings, but provides no code, no split indices, no error bars, and no reimplemented baselines.

Significance. If the reported metrics were robust and the comparison protocol valid, the proposed architecture would be a competitive segmentation baseline on a public dataset. The paper has the strength of using the public BUSI dataset and reporting its implementation settings in Table 2. However, the central empirical claim is currently not well supported: the evaluation uses a single image-level split on a dataset with multiple images per patient, and the 'outperformed all 15' claim rests on literature values from heterogeneous datasets and protocols. The LSTM's 'temporal' motivation is also not operationalized for static ultrasound images. These issues are load-bearing and require substantive revision.

major comments (4)
  1. [Result (data split and BUSI description)] The headline metrics (accuracy 98.88, Dice 92.74) rest on a single random 80/20 image-level split of BUSI, as stated in the Results section. BUSI has 780 images from 600 patients, so an image-level split can place images of the same patient in both training and test sets; because same-breast ultrasound images are highly correlated, this risks patient leakage and inflated segmentation metrics. No patient-level grouping, cross-validation, confidence intervals, or error bars are reported, and no code or split indices are provided. The reported numbers are therefore not yet auditable as estimates of generalization to new patients.
  2. [Table 3 and Results] The claim that the proposed model 'outperformed all 15' compared methods is not established. Table 3 assembles literature numbers from heterogeneous sources: [31] uses thermal images, [32] uses histopathology mitosis images, [33] uses histopathology images, and [19] uses DCE-MRI; these studies use different preprocessing, splits, and metric definitions, and none are reimplemented under the proposed BUSI 80/20 protocol. In addition, the text says 15 methods but lists 12 names, and Table 3 has 12 comparison rows (not counting U-Net++ and the proposed model). Direct comparability is thus unsupported even if the proposed model's own metrics are accurate.
  3. [Attention Block and Eq. (Mc(F))] The recurrent component is motivated as capturing 'temporal characteristics,' but the input to the LSTM consists of global average-pooled and max-pooled channel descriptors of a single static image (see the equation for Mc(F) and Figure 3). The BUSI dataset is static; no video frames, slices, or sequence dimension are defined. The LSTM is therefore unrolled over a constructed channel sequence, not over time. The paper should either define the temporal axis explicitly or reframe the contribution as a channel-attention variant, because the current text under-specifies the core architectural novelty.
  4. [Results prose vs Table 3] Multiple numbers in the text do not match Table 3, which makes the results section unreliable. Examples: the text gives CNN+LR accuracy as 06.87 while Table 3 shows 96.87; Faster R-CNN+CNNs precision is quoted as 97.60 in the text but 87.60 in Table 3; SaTransformer is credited with Specificity=89.51, Precision=89.51, and Sensitivity=89.51, while Table 3 lists Specificity=88.32 and no Sensitivity; GA-ANNs sensitivity is 90.00 in the text and 96.87 in Table 3; and the sentence 'proposed Unet++ approach ... accuracy of 92.70' contradicts the Unet++ row value of 98.58. The reported improvement over U-Net++ is also given as 0.0204 in Dice and 0.0304 in F1, whereas the table difference is about 2.04 and 3.04 percentage points. These inconsistencies should be corrected and a single audit trail provided.
minor comments (5)
  1. [Method (Xi,j equations)] The conditional definition for Xi,j with j > 0 is duplicated in the text, and the sentence 'In cases and j denotes...' is incomplete before the formula. The notation for encoder/decoder nodes should be cleaned up.
  2. [Figure captions 2 and 3] Figure 2 and Figure 3 have the same caption, 'The proposed channel attention mechanism,' even though Figure 3 appears to illustrate the LSTM cell. The captions should be corrected to match the content.
  3. [References] U-Net++ is cited as [19] in Table 1 and Related Works but as [26] in the Multiscale Feature Extraction Module; [26] actually refers to ResUNet++. The reference numbering should be made consistent.
  4. [Results] The phrase 'Using tabularx and booktabs' appears in the Results section and is a leftover LaTeX instruction that should be removed.
  5. [Related Works] There is a typo 'in the field of Business (BUSI)', and the paragraph about Dataset B and the STU dataset does not define these datasets; also the AAU-net comparison is confusingly worded and should be rewritten.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported segmentation metrics are measured against external BUSI ground truth, and the paper contains no self-citations, no fitted-input-as-prediction steps, and no definitional equivalence between the architecture equations and the reported results.

full rationale

This is an empirical architecture-evaluation paper, and no claimed result reduces to its own inputs. (1) No self-citation: references [1]-[39] are all external works; none is authored by Hesaraki, Akbari, or Mousa, so the self-citation patterns (load-bearing self-citation, uniqueness imported from authors, ansatz smuggled via citation) do not arise. (2) The headline metrics (accuracy 98.88, Dice 92.74) are measured on a held-out 20% slice of the public BUSI dataset against external ground-truth masks; hyperparameters are stated in Table 2 and no parameter is described as fitted to the test set, so "fitted input called prediction" does not apply. (3) The architectural equations (UNet++ nested skip connections Xi,j, CBAM channel/spatial attention Mc/Ms, LSTM gate equations, loss L = λ1Lseg + λ2LDC) are standard formulas; none defines the reported metrics in terms of themselves, so no self-definitional step exists. (4) The auxiliary distance-class loss derives its supervision by a distance transform of the same ground-truth mask; this is auxiliary supervision, not a predicted quantity that is circular. Issues that I flag as correctness and reproducibility risks rather than circularity: Table 3 compares literature numbers from heterogeneous datasets and protocols (DCE-MRI, histopathology, thermal images, BUSI subsets) without reimplementation; evaluation is a single image-level 80/20 split of 780 images from 600 patients, with no patient grouping, split indices, or error bars; and the LSTM's temporal axis is never defined for a static-image dataset. Citation-accuracy problems also exist (UNet++ is attributed to [26], which lists ResUNet++; convolution definitions are attributed to [37]-[39], which are breast-imaging studies; CBAM is adopted without citation), but these are misattributions and omissions, not self-referential load-bearing support.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The central claim depends mainly on hyperparameters and evaluation assumptions, not on new theoretical postulates. The paper introduces no new particles, forces, or entities; its novel content is the architecture combination and empirical results.

free parameters (7)
  • image_size = 128x128
    All BUSI images were resized to 128 by 128 pixels; no ablation shows robustness to this choice.
  • batch_size = 16
    Table 2 lists batch size 16 without justification or sensitivity analysis.
  • learning_rate = 0.001
    Table 2 lists Adam learning rate 0.001; no sweep is reported.
  • dropout_rate = 0.5
    Table 2 lists dropout 0.5; no ablation is reported.
  • LSTM_block_size = 16
    Table 2 lists LSTM block 16; the number of recurrent units or sequence handling is not justified.
  • train_test_split_ratio = 80/20
    The Results section says 80% training and 20% testing; this is a hand-chosen split with no patient-level grouping described.
  • lambda_1_and_lambda_2_loss_weights
    The loss section says the weights are learnable, but their fitted values and effects are not reported.
assumptions (4)
  • domain assumption Ground truth masks in BUSI are accurate enough to serve as labels.
    The reported Dice and related scores treat the provided segmentation masks as correct references; no mask quality control is described. This enters in the Results section where the dataset is described.
  • domain assumption Metrics from the cited papers in Table 3 were produced under protocols comparable to the proposed 80/20 split.
    The superiority claim requires comparability across papers, but the cited works use different datasets, preprocessing, and splits. None are reimplemented here, and this is the weakest load-bearing premise.
  • ad hoc to paper Feeding average-pooled and max-pooled channel descriptors through an LSTM gives a meaningful temporal or sequential representation.
    The Attention Block section presents the LSTM as capturing temporal information, yet the input is a static feature map and no sequence ordering is defined.
  • standard math Backpropagation and Adam optimization converge to a usable local optimum.
    The method assumes standard deep learning training works; this is conventional for this literature.

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Cite this review

Pith. "Pith review of UNet++ and LSTM combined approach for Breast Ultrasound Image Segmentation." pith.science (2026). https://pith.science/paper/QHZYEIKI

@misc{pith2026241205585,
  author       = {Pith},
  title        = {Pith review of: UNet++ and LSTM combined approach for Breast Ultrasound Image Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QHZYEIKI}},
  note         = {Machine review of arXiv:2412.05585}
}
read the original abstract

Breast cancer stands as a prevalent cause of fatality among females on a global scale, with prompt detection playing a pivotal role in diminishing mortality rates. The utilization of ultrasound scans in the BUSI dataset for medical imagery pertaining to breast cancer has exhibited commendable segmentation outcomes through the application of UNet and UNet++ networks. Nevertheless, a notable drawback of these models resides in their inattention towards the temporal aspects embedded within the images. This research endeavors to enrich the UNet++ architecture by integrating LSTM layers and self-attention mechanisms to exploit temporal characteristics for segmentation purposes. Furthermore, the incorporation of a Multiscale Feature Extraction Module aims to grasp varied scale features within the UNet++. Through the amalgamation of our proposed methodology with data augmentation on the BUSI with GT dataset, an accuracy rate of 98.88%, specificity of 99.53%, precision of 95.34%, sensitivity of 91.20%, F1-score of 93.74, and Dice coefficient of 92.74% are achieved. These findings demonstrate competitiveness with cutting-edge techniques outlined in existing literature.

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Reference graph

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