Quantum-enhanced unsupervised image segmentation for medical images analysis

Laia Domingo; Mahdi Chehimi

REVIEW 4 major objections 5 minor 1 cited by

Quantum-enhanced unsupervised image segmentation for medical images analysis

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read According to the paper, quantum annealing can segment mammograms about as accurately as a classical solver while running roughly ten times faster, and the unsupervised pipeline's masks land close to supervised UNet quality.

desk verdict The paper's QUBO objective is algebraically degenerate—alpha is redundant and the global minima are trivial—so the reported segmentation results cannot be optima of the written objective. read the letter →

arxiv 2411.15086 v1 pith:WZPGJWCQ submitted 2024-11-22 eess.IV cs.CVquant-ph

Laia Domingo , Mahdi Chehimi This is my paper · ORCID

classification eess.IVcs.CVquant-ph

keywords quantumannealingimagesegmentationQUBOunsupervisedlearningmammographyvariationalcircuitsmedicalanalysisquantum-inspiredrepresentation

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

The paper proposes a complete unsupervised pipeline for segmenting lesions in mammograms, built around a quantum-inspired preprocessing step and a quadratic unconstrained binary optimization (QUBO) formulation of the segmentation task. It claims that solving that QUBO with quantum annealing or with variational quantum circuits gives masks whose Dice and IoU scores match a classical numerical solver, that quantum annealing runs about ten times faster than that solver in their experiments, and that the unsupervised masks land close to those of a supervised U-shaped network (UNet) and its stronger residual variant. If these claims hold, quantum annealing would offer a label-free route to medical-image segmentation with accuracy comparable to supervised deep learning, which matters in settings where annotated datasets are scarce or expensive. The paper also reports that its quantum-inspired image representation sharpens lesion boundaries and cuts the training epochs of supervised models by about a third when used as their input.

What carries the argument

The load-bearing object is the segmentation QUBO: for binary pixel labels $x_i$, the cost sums over neighboring pixels $W_{ij}[(x_i+x_j-2x_ix_j)+\alpha(1-\delta(x_i,x_j))]$, where $W_{ij}$ is a Gaussian similarity weight, $\delta(x_i,x_j)$ is the Kronecker delta (one when the two labels match), the first term cuts weakly similar pairs apart, and the $\alpha$ term is intended to encourage smooth masks. The second piece is the quantum-inspired image transform, a single-pass filter that reweights each pixel by its local contrast and neighborhood intensity sum through a multilevel sigmoid, producing the enhanced image that feeds the graph. The QUBO is solved three ways: simulated annealing, quantum annealing on a physical processor, and a variational circuit that amplitude-encodes one pixel per basis state with an ancilla qubit and treats the measured probability $|\beta_i|^2$ as the pixel's class score. The classical baselines are a classical optimization solver, a thresholding method, and two supervised neural networks.

What would settle it

Evaluate Eq. 15 over all binary labelings of a small graph at $\alpha=0.1$ and $\alpha=100$: since for binary $x_i,x_j$ one has $1-\delta(x_i,x_j)=x_i+x_j-2x_ix_j$, the loss is $(1+\alpha)$ times the min-cut term, so the optimal labeling is identical at both values, which would refute the claimed trade-off that the paper uses to set $\alpha=0.1$.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a segmentation mask can be recovered without any labeled data by mapping a quantum-enhanced image to a weighted grid graph, writing a min-cut-with-smoothness objective as a QUBO, and minimizing that QUBO with quantum annealing or a variational circuit. On 42x42 pixel mammography crops, quantum annealing reaches Dice/IoU of 0.84/0.74 and the variational approach 0.83/0.73, essentially matching the classical solver's 0.84/0.74, while clearly beating the histogram-thresholding baseline (0.75/0.62). Those numbers sit just below the supervised UNet (0.85/0.75) and ResUNet (0.89/0.81), which is the paper's evidence that unsupervised quantum optimization can approach supervised state-of-the-art. The average execution time of quantum annealing is reported as an order of magnitude shorter than the classical solver's, with lower variance, which the paper presents as the concrete practical advantage of the quantum step.

Load-bearing premise

The load-bearing premise is that the smoothness term in Eq. 15 is genuinely different from the min-cut term, so that the tuning parameter $\alpha$ really changes which segmentation is best; if the two terms coincide for binary pixels, the $\alpha$-tuning story and the reported coefficients do not follow.

Editorial extensions

If this is right

Quantum annealing can serve as an unsupervised segmentation optimizer for small mammography crops, matching the classical solver's accuracy while reducing average execution time by roughly an order of magnitude.
The full unsupervised pipeline reaches Dice/IoU scores close to those of supervised UNet on this dataset, implying that comparable mask quality can be obtained without expert annotations or network training.
The quantum-inspired preprocessing alone improves supervised training: UNet/ResUNet trained on the transformed images converge in about 30 epochs instead of 45, with slightly higher IoU.
The variational circuit uses only a logarithmic number of qubits for the pixel indices, so if runtime on real hardware drops, it is the most scalable of the quantum options considered here.

Reading between the lines

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

Beyond the paper, the claimed speedup is tied to the tested image size and solver settings; a fairer comparison would report quantum annealing with embedding and sampling overhead against the classical solver under matched optimality tolerances, and repeat the timing on larger grids.
Beyond the paper, the quantum-inspired transform may be doing much of the accuracy work; running the same QUBO pipeline with and without that transform, and with the classical solver in place of quantum annealing, would attribute the gain between preprocessing and solver.
Beyond the paper, the variational circuit's logarithmic qubit count suggests a natural scaling test: if real quantum hardware removes the simulation overhead, the same encoding could be pushed to much larger segmentation grids, where the classical solver's runtime grows steeply.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Dear colleague,

This one should not be taken at face value. The central QUBO, Eq. 15, has a serious algebraic identity: for binary variables, the smoothness term exactly equals the min-cut term. Alpha just rescales the whole objective and cannot influence the argmin, so the alpha-sweep experiment in Section 2.2.1 is vacuous. Worse, the objective has no unary or data term. With nonnegative Gaussian edge weights, the global minima are the all-zero and all-one masks. The reported Dice/IoU scores around 0.84/0.74 cannot come from minimizing this objective. Either an undisclosed unary term exists or the experiments used a different objective. That is not a minor typo; it undermines the paper's core claim that quantum annealing and VQA solve this segmentation QUBO.

What is genuinely new is the end-to-end unsupervised pipeline: the Konar-style quantum-inspired image transform feeding a QUBO min-cut formulation, evaluated on INbreast mammography crops with a broad side-by-side of simulated annealing, D-Wave QA, VQA, Gurobi, Otsu, UNet, and ResUNet. The writing is clear, the dataset is public, and Table 1's finding that the quantum-inspired transform speeds up UNet convergence is a useful empirical observation. Code availability is promised, which is good practice.

The soft spots extend beyond the QUBO bug. The timing comparison pits QA against Gurobi, a general-purpose solver, when a specialized graph-cut solver such as Boykov-Kolmogorov is the natural baseline; an order-of-magnitude speedup claim is misleading without that comparison. Hyperparameters alpha, sigma, p, T, and mu are tuned on the test set, and no error bars are reported. The experiments use 42x42 downsampled images, far from clinical resolution, so the "comparable to supervised UNet" claim is narrow. The stress-test note is correct: the alpha-redundancy objection is a symptom of the deeper misspecification—no unary term exists.

The empirical scaffolding looks genuine, but the load-bearing math does not hold. The right response is a major revision: either add a proper data term and show the derivation, or state the actual objective that was minimized. If the implementation includes an unstated unary term, that must be disclosed before the timing and hyperparameter claims can be fairly assessed.

This paper deserves a serious referee because the topic is timely and the comparison is broad, but I would not cite it in its current form.

Referee Report

4 major / 5 minor

Summary. The paper proposes an end-to-end unsupervised pipeline for segmenting breast masses in mammograms. It combines a quantum-inspired image transformation (Sect. 4.2), a QUBO formulation of a min-cut plus smoothness objective (Eq. 15), and several solvers: simulated annealing, quantum annealing, and variational quantum circuits, benchmarked against Gurobi, Otsu thresholding, and supervised UNet/ResUNet models on the INbreast dataset. The central claims are that the quantum annealing and VQA results are comparable to the classical Gurobi optimizer, that quantum annealing is an order of magnitude faster, and that the unsupervised pipeline approaches the accuracy of supervised UNet-based methods.

Significance. If the claims were correct, this would be a meaningful demonstration of quantum annealing and variational circuits for medical image segmentation, with a public dataset and standard metrics. The experimental scope is appropriate, and the quantum-inspired image representation is evaluated independently on supervised models, which is a useful self-contained contribution. However, the paper ships no reproducible code or machine-checked derivations, and the core QUBO objective as written is algebraically degenerate: the smoothness term is identical to the min-cut term for binary variables, and the objective has no data-attachment term, so its global minima are the empty and full masks rather than the reported ROI segmentations. Because the reported performance numbers and speed comparisons rest on this ill-posed objective, the main claims are not supported as written.

major comments (4)

[Sect. 4.3.1, Eq. (15)] For binary variables, (1 - (x_i + x_j - 1)^2) = x_i + x_j - 2 x_i x_j, exactly the same expression as |x_i - x_j|. Therefore the term multiplied by alpha is identical to the min-cut term, so alpha merely rescales the total objective and cannot change the optimal binary solution. The alpha sweep in Sect. 2.2.1 ("Effect of alpha") is vacuous, and the statement that alpha=0.1 provides the best balance is not supported. Furthermore, the QUBO coefficients in Eq. (16) do not follow from Eq. (15): with the stated Q_ij = -2(1+alpha) W_ij and the standard x^T Q x convention, the effective cross-coefficient would be -4(1+alpha) W_ij, not -2(1+alpha) W_ij, while the claimed linear coefficient (2alpha+1) sum_j W_ij together with Q_ii = -alpha sum_j W_ij only reproduces (alpha+1) sum_j W_ij after absorbing the diagonal. The algebraic mismatch must be resolved before any experimental comparison is meaningful.
[Sect. 4.3.1, Eq. (15), and Sect. 2.2.2, Table 2] The objective in Eq. (15) contains no unary or data-attachment term tied to pixel intensities, no source/sink term, and no label-balance constraint. Since all edge weights W_ij are Gaussian similarities (Eq. 10) and hence nonnegative, L(0,...,0) = L(1,...,1) = 0, while every nonconstant binary labeling has strictly positive cost. Thus the global minima of the written objective are the trivial empty and full masks, not the ROI masks whose Dice/IoU values are reported in Table 2. The same issue affects the VQA relaxation in Eq. (20). The experiments therefore cannot be minimizing the written QUBO; either an undisclosed unary term or constraint was used, or the reported masks are not optima of Eq. (15). This undermines the central accuracy and speed claims, including the comparison against Gurobi and the claim that quantum annealing is an order of magnitude faster.
[Sects. 2.1, 2.2.1, 4.2] Several key hyperparameters are selected on the same test set used to report final scores: alpha is chosen by sweeping over the test set (Sect. 2.2.1), p=0.9 is chosen after "thorough experimentation" (Sect. 4.2), T=0.3 for the VQA warm start is fixed without a separate validation procedure, and \hat{sigma} = 0.5 std(z) is data-dependent. This is mild test-set fitting, but it matters because the reported Dice/IoU differences among QA, VQA, and Gurobi are small (Table 2), and the paper provides no confidence intervals or statistical tests. At minimum, the authors should state which images were used for hyperparameter selection and which for scoring, or use a nested validation split.
[Sect. 2.2.3 and Sect. 4.3.3] The execution-time comparison is reported for a single 42x42 grid (1764 pixels) and for D-Wave with 2000 annealing runs, but the paper does not specify whether the reported quantum annealing time includes embedding, QPU access, or post-processing, and Figure 5 appears to lack units and error bars. The claim that quantum annealing is an order of magnitude faster than Gurobi therefore lacks the detail needed to assess fairness and reproducibility. The authors should report the exact timing methodology, the number of images, and the variance across runs, and should clarify whether Gurobi is given the same warm-start or optimality-tolerance settings.

minor comments (5)

[Abstract] There is a typo: "computational resourcess" should be "computational resources".
[Eq. (3)] The text says "optimizes both the Dice ans IoU scores"; "ans" should be "and".
[Sect. 4.2, Eqs. (4)-(6)] The notation for the quantum-inspired transformation is difficult to parse: the inner-product-like expression I_{ij} times the state overlaps is not defined precisely, and the rendering of Eq. (4) is garbled. The authors should rewrite the definition with standard bra-ket notation or explicit scalar functions.
[Tables 1 and 2] Table 1 reports UNET on original images with Dice 0.911, while Table 2 reports UNET with Dice 0.85; the discrepancy is not explained. The authors should clarify whether the two tables use different image resolutions, different test subsets, or different training configurations.
[Sect. 4.3.1, Eq. (15)] The text refers to a "Potts model" smoothness penalty, but the penalty 1 - delta(x_i, x_j) is identical to the min-cut term for binary variables. The authors should either introduce a genuinely different smoothness term (e.g., based on higher-order interactions or a label-cost penalty) or remove the claim that the two objectives are competing.

Circularity Check

1 steps flagged · score 6.0 of 10

Eq. 15's smoothness term is algebraically identical to its min-cut term, making the alpha sweep vacuous and the written QUBO's minimizers trivial; the reported ROI masks therefore do not follow from the stated objective.

self definitional [Section 4.3.1, Eq. 15; alpha experiment in Section 2.2.1]
"we incorporate a smoothness penalty than ensures a cohesive image segmentation masks, Following Potts model [45]... Thus, the final loss function is L(⃗x)=∑_{(i,j)∈E} W_ij[(x_i+x_j−2x_ix_j)+α(1−(x_i+x_j−1)^2)], where δ denotes the Kronecker delta and α is a hyperparameter controlling the importance of the smoothness term compared to the min-cut term."

For binary x_i, 1−(x_i+x_j−1)^2 = x_i+x_j−2x_ix_j, so Eq. 15 is (1+α) times the min-cut QUBO in Eq. 14. The 'smoothness' term is therefore the same function as the min-cut term by construction; α cannot change the argmin. The Section 2.2.1 claim that α=0.1 gives the best balance is an artifact of this identity (and solver noise), not of tuning two distinct objectives. The same collapse invalidates Eq. 16's stated Q_ii and c_i coefficients. Since W_ij are nonnegative Gaussian similarities, the written objective has global minima at the all-zero and all-one masks, so the ROI masks with Dice/IoU ≈0.84/0.74 in Table 2 cannot be solutions of the stated QUBO; the reported segmentation results do not derive from the paper's first-principles objective.

full rationale

The central benchmark (quantum annealing vs. Gurobi, and both vs. UNet) is an external empirical comparison and is not itself circular: the timing advantage and Dice/IoU numbers would be meaningful if the QUBO were correctly specified. However, the derivation chain leading to those numbers collapses at Eq. 15. The alpha term and min-cut term are termwise equal for binary variables, so the alpha sweep is vacuous, and the stated objective has no pixel-intensity/data term, making its global optima the empty and full masks rather than the reported lesions. The paper's own Eq. 16 coefficients do not follow from Eq. 15. This is a construction-level reduction of the claimed 'smoothness balancing' to the min-cut term, hence partial circularity (score 6) rather than a fully circular self-citation chain. There is no load-bearing self-citation: the cited quantum-inspired representation [27,28] and min-cut formulation [41,42] are external prior work. A separate, milder concern is that alpha, sigma, p, and T are selected while looking at the same test set later used for Table 2, which is an evaluation-protocol issue but not the main circularity.

Assumptions & free parameters 6 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities, particles, or forces. Its freedom lies in six hyperparameters, several of which are tuned directly on the test set. The key conceptual assumption is that the QUBO min-cut plus smoothness model captures mammography mass segmentation, and that the D-Wave annealer solves the resulting instances faithfully. No independent evidence is given for these assumptions beyond the reported test-set numbers.

free parameters (6)

alpha (smoothness weight) = 0.1
Tuned on the test set (Section 2.2.1) using simulated annealing; claimed to balance min-cut vs smoothness, but mathematically redundant for binary variables.
sigma (Gaussian similarity scale) = 0.5 * std(z)
Chosen by grid-search parameter optimization (Section 4.3), affecting all edge weights Wi,j in the QUBO.
p (percentile for omega distribution) = 0.9
Selected after 'thorough experimentation' (Section 4.2) to set the gray-scale class thresholds in the quantum-inspired transform.
T (VQA warm-start threshold) = 0.3
Heuristically fixed (Section 4.3.4) to initialize VQA from the transformed image; strongly influences the VQA result.
mu (sigmoid steepness) = 0.4
Fixed hyperparameter in the multilevel sigmoid activation (Eq. 7), affecting the quantum-inspired representation.
L (number of gray-scale classes) = 8
Chosen for the quantum-inspired transform (Section 4.2), controlling the multilevel segmentation representation.

assumptions (3)

domain assumption QUBO min-cut with a smoothness term is a valid model for mass segmentation in mammograms
The paper assumes that minimizing the weighted cut with a Potts-like smoothness term on a 4-neighbor grid yields meaningful tumor masks for 42x42 crops (Section 4.3). This is not derived from imaging or clinical principles.
domain assumption D-Wave quantum annealer returns solutions of sufficient quality for QUBO instances with 1764 variables
The paper assumes 2000 runs on the Pegasus sampler adequately solve the QUBO (Section 4.3.3); no optimality verification or comparison to an exact solver is provided.
domain assumption A single iteration of the quantum-inspired transformation is sufficient
The authors state that one iteration suffices, deviating from Konar et al.'s iterative procedure (Section 4.2). This assumption underpins the preprocessing step.

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Cite this review

Pith. "Pith review of Quantum-enhanced unsupervised image segmentation for medical images analysis." pith.science (2026). https://pith.science/paper/WZPGJWCQ

@misc{pith2026241115086,
  author       = {Pith},
  title        = {Pith review of: Quantum-enhanced unsupervised image segmentation for medical images analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WZPGJWCQ}},
  note         = {Machine review of arXiv:2411.15086}
}

read the original abstract

Breast cancer remains the leading cause of cancer-related mortality among women worldwide, necessitating the meticulous examination of mammograms by radiologists to characterize abnormal lesions. This manual process demands high accuracy and is often time-consuming, costly, and error-prone. Automated image segmentation using artificial intelligence offers a promising alternative to streamline this workflow. However, most existing methods are supervised, requiring large, expertly annotated datasets that are not always available, and they experience significant generalization issues. Thus, unsupervised learning models can be leveraged for image segmentation, but they come at a cost of reduced accuracy, or require extensive computational resourcess. In this paper, we propose the first end-to-end quantum-enhanced framework for unsupervised mammography medical images segmentation that balances between performance accuracy and computational requirements. We first introduce a quantum-inspired image representation that serves as an initial approximation of the segmentation mask. The segmentation task is then formulated as a QUBO problem, aiming to maximize the contrast between the background and the tumor region while ensuring a cohesive segmentation mask with minimal connected components. We conduct an extensive evaluation of quantum and quantum-inspired methods for image segmentation, demonstrating that quantum annealing and variational quantum circuits achieve performance comparable to classical optimization techniques. Notably, quantum annealing is shown to be an order of magnitude faster than the classical optimization method in our experiments. Our findings demonstrate that this framework achieves performance comparable to state-of-the-art supervised methods, including UNet-based architectures, offering a viable unsupervised alternative for breast cancer image segmentation.

Figures

Figures reproduced from arXiv: 2411.15086 by the authors.

**Figure 2.** Example quantum-inspired image representations (first column), the original images (second column) and the ROI masks (third column). While this strategy offers a modest improvement in performance and reduces the number of training epochs compared to the fully classical UNET models, it still relies on a supervised approach, therefore needing an annotated dataset to train the neural networks. Therefore, we explore uns… view at source ↗

**Figure 3.** Effect of the value α on the performance of the optimization problem using simulated annealing. the Dice coefficient and the Intersection over Union (IoU) score, is achieved when α = 0.1. When α is set to low values, the resulting segmentation masks tend to be overly fragmented, with noticeable gaps within the detected regions of interest (ROIs). Conversely, at high values of α, the optimization process becomes over… view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Examples of different image segmentation masks produced by the Otsu thresholding method, the quantum annealing method and the ground truth. an improvement over the Otsu method. One of the key improvements is that it successfully identifies only one connected component …

**Figure 5.** Figure 5: Execution times for the different image segmentation models used in this work. 3 Conclusion and Future Work This study investigates various image segmentation methodologies, including classical, quantum-inspired, and quantum computing approaches, for breast cancer dete…

**Figure 6.** Figure 6: Choice of hyperparameter ω, that controls the gray-scale distribution across the L segmentation classes. 4.3 Image Segmentation The resulting image is then provided as input to an unsupervised segmentation algorithm to identify the ROI within the mammography image. To …

**Figure 7.** Figure 7: Variational quantum circuit design. Notice that this state can be easily encoded in the quantum circuit just by combining X and Hadamard gates. In this scenario, the initial state is selected such that xi = 1 if the intensity of pixel i in the quantum-inspired transfor…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Superpixel-Based QUBO for Scalable Quantum-Enhanced Medical Image Segmentation
cs.CV 2026-07 conditional novelty 4.0 of 10

Superpixel RAG QUBO on full-resolution INbreast mammograms raises mean IoU from 0.73 to 0.76 while shrinking the problem from ~1764 to ~48 variables and runtime from ~22s to ~0.67s versus downsampled pixel QUBO.

Reference graph

Works this paper leans on

49 extracted references · 34 canonical work pages · cited by 1 Pith paper

[1]

Z., Alotaibi, F

Ahmad, S., Asghar, M. Z., Alotaibi, F. M. & Alotaibi, Y . D. Diagnosis of cardiovascular disease using deep learning technique. Soft Comput. 27, 8971–8990 (2023)

work page 2023
[2]

Hussain, I., Nazir, M. B. et al. Mind matters: Exploring ai, machine learning, and deep learning in neurological health. Int. J. Adv. Eng. Technol. Innov. 1, 209–230 (2024)

work page 2024
[3]

Tsietso, D. et al. Multi-input deep learning approach for breast cancer screening using thermal infrared imaging and clinical data. IEEE Access 11, 52101–52116 (2023)

work page 2023
[4]

Hendrix, N. et al. Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist. Eur. Radiol. 33, 1575–1588 (2023)

work page 2023
[5]

& Singh, A

Jyothi, P. & Singh, A. R. Deep learning models and traditional automated techniques for brain tumor segmentation in mri: a review. Artif. intelligence review 56, 2923–2969 (2023)

work page 2023
[6]

L., Miller, K

Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2020. CA: A Cancer J. for Clin. 70, 7–30, DOI: 10.3322/caac. 21590 (2020)

doi:10.3322/caac 2020
[7]

Organization, W. H. Breast cancer. https://www.who.int/news-room/fact-sheets/detail/breast-cancer (2024). Accessed: 2024-10-10

work page 2024
[8]

Lauby-Secretan, B. et al. Breast-cancer screening—viewpoint of the iarc working group. The New Engl. J. Medicine 372, 2353–2358, DOI: 10.1056/NEJMsr1504363 (2015)

work page doi:10.1056/nejmsr1504363 2015

Show all 49 references

[9]

A threshold selection method from gray-level histograms

Otsu, N. A threshold selection method from gray-level histograms. IEEE Transactions on Syst. Man, Cybern. 9, 62–66, DOI: 10.1109/TSMC.1979.4310076 (1979)

1979
[10]

Shapiro, L. G. & Stockman, G. C. Computer Vision (Prentice Hall, 2001)

2001
[11]

Liang, Y . & Fu, J. Watershed algorithm for medical image segmentation based on morphology and total variation model. Int. J. Pattern Recognit. Artif. Intell. 33, 1954019 (2019). 14/16

2019
[12]

Graph-based region and boundary aggregation for biomedical image segmentation

Meng, Y .et al. Graph-based region and boundary aggregation for biomedical image segmentation. IEEE transactions on medical imaging 41, 690–701 (2021)

2021
[13]

Wang, R. et al. Medical image segmentation using deep learning: A survey. IET image processing 16, 1243–1267 (2022)

2022
[14]

& Darrell, T

Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition , 3431–3440 (2015)

2015
[15]

D., Hager, G

Zhu, W., Xiang, X., Tran, T. D., Hager, G. D. & Xie, X. Adversarial deep structured nets for mass segmentation from mammograms. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) , 847–850 (IEEE, 2018)

2018
[16]

A medical image segmentation method based on multi-dimensional statistical features

Xu, Y .et al. A medical image segmentation method based on multi-dimensional statistical features. Front. Neurosci. 16, 1009581 (2022)

2022
[17]

& Brox, T

Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI) , vol. 9351, 234–241, DOI: 10.1007/978-3-319-24574-4_ 28 (Springer, Cham, 2015)

doi
[18]

Efficient skin lesion segmentation using separable-unet with stochastic weight averaging

Tang, P.et al. Efficient skin lesion segmentation using separable-unet with stochastic weight averaging. Comput. Methods Programs Biomed. 178, 289–301, DOI: 10.1016/j.cmpb.2019.07.005 (2019)

2019 doi
[19]

& Luo, W

Li, S., Chen, Y ., Yang, S. & Luo, W. Cascade dense-unet for prostate segmentation in mr images. In International Conference on Intelligent Computing, 481–490, DOI: 10.1007/978-3-030-26763-6_46 (Springer, Cham, 2019)

2019 doi
[20]

Baccouche, A. et al. Connected-unets: a deep learning architecture for breast mass segmentation. npj Breast Cancer 7, 151, DOI: 10.1038/s41523-021-00358-x (2021)

2021 doi
[21]

B., Kaabouch, N., Saidi, M

Soulami, K. B., Kaabouch, N., Saidi, M. N. & Tamtaoui, A. Breast cancer: one-stage automated detection, segmentation, and classification of digital mammograms using unet model based-semantic segmentation.Biomed. Signal Process. Control. 66, 102481, DOI: 10.1016/j.bspc.2021.102...

2021
[22]

& Ben Ishak, A

Tariq Jamal, A., Abdel-Khalek, S. & Ben Ishak, A. Multilevel segmentation of medical images in the framework of quantum and classical techniques. Multimed. Tools Appl. 82, 13167–13180 (2023)

2023
[23]

Gurobi Optimizer Reference Manual (2024)

Gurobi Optimization, LLC. Gurobi Optimizer Reference Manual (2024)

2024
[24]

& Chanu, Y

Dhanachandra, N., Manglem, K. & Chanu, Y . J. Image segmentation using k-means clustering algorithm and subtractive clustering algorithm. Procedia Comput. Sci. 54, 764–771 (2015)

2015
[25]

& Haltmeier, M

Schwab, M., Mayr, A. & Haltmeier, M. Deep gaussian mixture model for unsupervised image segmentation (2024). 2404.12252

2024 arXiv
[26]

& Proctor, T

Hothem, D., Hines, J., Nataraj, K., Blume-Kohout, R. & Proctor, T. Predictive models from quantum computer benchmarks. In 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) , vol. 01, 709–714, DOI: 10.1109/QCE57702.2023.00086 (2023)

2023
[27]

Konar, D., Bhattacharyya, S., Gandhi, T. K. & Panigrahi, B. K. A quantum-inspired self-supervised network model for automatic segmentation of brain mr images. Appl. Soft Comput. 93, 106348, DOI: 10.1016/j.asoc.2020.106348 (2020)

2020
[29]

Presles, T. et al. Synthetic aperture radar image segmentation with quantum annealing. IET Radar , Sonar & Navig. 18, 812–824, DOI: 10.1049/rsn2.12523 (2024)

2024 doi
[30]

M., Macaluso, A., Nuske, M., Klusch, M

Venkatesh, S. M., Macaluso, A., Nuske, M., Klusch, M. & Dengel, A. Q-seg: Quantum annealing-based unsupervised image segmentation. IEEE Comput. Graph. Appl. (2024)

2024
[31]

Wang, K. et al. Implementation and analysis of quantum-classical hybrid interactive image segmentation algorithm based on quantum annealer. Quantum Inf. Process. 23, 301 (2024)

2024
[32]

Moreira, I. C. et al. Inbreast: toward a full-field digital mammographic database. Acad. Radiol. 19, 236–248, DOI: 10.1016/j.acra.2011.09.014 (2012). 15/16

2012 doi
[33]

Konar, D., Bhattacharyya, S., Gandhi, T. K. et al. 3d quantum-inspired self-supervised tensor network for volumetric segmentation of brain mr images. TechRxiv DOI: 10.36227/techrxiv.12909860.v4 (2023). Preprint

2023 doi
[34]

& Orús, R

Mugel, S. & Orús, R. Compactifai: Extreme compression of large language models. arXiv:2401.14109 (2024)

2024 arXiv
[36]

Kim, Y .-D. et al. Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv:1511.06530 (2015)

2015 arXiv
[37]

& Ran, S.-J

Qing, Y ., Li, K., Zhou, P.-F. & Ran, S.-J. Compressing neural network by tensor network with exponentially fewer variational parameters. arXiv:2305.06058 (2024)

2024 arXiv
[38]

& Elmaghraby, A

Baccouche, A., Garcia-Zapirain, B., Castillo Olea, C. & Elmaghraby, A. S. Breast lesions detection and classification via yolo-based fusion models. Comput. Mater. Continuum 69, 1407–1425 (2021)

2021
[39]

& Muller, K

Pizer, S., Johnston, R., Ericksen, J., Yankaskas, B. & Muller, K. Contrast-limited adaptive histogram equalization: speed and effectiveness. In [1990] Proceedings of the First Conference on Visualization in Biomedical Computing , 337–345, DOI: 10.1109/VBC.1990.109340 (1990)

1990
[40]

& Dutta, P

Bhattacharyya, S., Maulik, U. & Dutta, P. Multilevel image segmentation with adaptive image context based thresholding. Appl. Soft Comput. 11, 946–962, DOI: 10.1016/j.asoc.2010.01.015 (2011)

2011 doi
[41]

M., Macaluso, A., Nuske, M., Klusch, M

Venkatesh, S. M., Macaluso, A., Nuske, M., Klusch, M. & Dengel, A. Q-seg: Quantum annealing-based unsupervised image segmentation (2023). 2311.12912

2023 arXiv
[43]

Ising formulations of many np problems

Lucas, A. Ising formulations of many np problems. Front. physics 2, 5 (2014)

2014
[44]

McGeoch, C. C. Theory versus practice in annealing-based quantum computing. Theor. Comput. Sci. 816, 169–183 (2020)

2020
[45]

& Zabih, R

Boykov, Y ., Veksler, O. & Zabih, R. Fast approximate energy minimization via graph cuts. In IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, 1222–1239, DOI: 10.1109/34.969114 (2001)

2001 doi
[46]

Hastings, W. K. Monte carlo sampling methods using markov chains and their applications. Biometrika 57, 97–109, DOI: 10.1093/biomet/57.1.97 (1970)

1970 doi
[47]

Perelshtein, M. R. et al. NISQ-compatible approximate quantum algorithm for unconstrained and constrained discrete optimization. Quantum 7, 1186 (2023)

2023
[48]

M., Macaluso, A., Nuske, M., Klusch, M

Venkatesh, S. M., Macaluso, A., Nuske, M., Klusch, M. & Dengel, A. Quantum annealing-based algorithm for efficient coalition formation among leo satellites (2024). 2408.06007

2024 arXiv
[49]

M., Macaluso, A., Nuske, M., Klusch, M

Venkatesh, S. M., Macaluso, A., Nuske, M., Klusch, M. & Dengel, A. Qubit-efficient variational quantum algorithms for image segmentation (2024). 2405.14405

2024 arXiv
[50]

& Hirota, K

Le, P., Iliyasu, A., Dong, F. & Hirota, K. A flexible representation of quantum images for polynomial preparation, image compression and processing operations, quantum inf. Quantum Inf. Process. 10, 63–84, DOI: 10.1007/s11128-010-0177-y (2011)

2011 doi
[51]

& Wang, Y

Zhang, Z., Liu, Q. & Wang, Y . Road extraction by deep residual u-net. InIEEE Geoscience and Remote Sensing Letters , vol. 15, 749–753, DOI: 10.1109/LGRS.2018.2802944 (2018)

2018
[52]

Moreira, I. C. et al. Inbreast: toward a full-field digital mammographic database. Acad. radiology 19, 236–248 (2012). 16/16

2012

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Z., Alotaibi, F

Ahmad, S., Asghar, M. Z., Alotaibi, F. M. & Alotaibi, Y . D. Diagnosis of cardiovascular disease using deep learning technique. Soft Comput. 27, 8971–8990 (2023)

work page 2023

[2] [2]

Hussain, I., Nazir, M. B. et al. Mind matters: Exploring ai, machine learning, and deep learning in neurological health. Int. J. Adv. Eng. Technol. Innov. 1, 209–230 (2024)

work page 2024

[3] [3]

Tsietso, D. et al. Multi-input deep learning approach for breast cancer screening using thermal infrared imaging and clinical data. IEEE Access 11, 52101–52116 (2023)

work page 2023

[4] [4]

Hendrix, N. et al. Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist. Eur. Radiol. 33, 1575–1588 (2023)

work page 2023

[5] [5]

& Singh, A

Jyothi, P. & Singh, A. R. Deep learning models and traditional automated techniques for brain tumor segmentation in mri: a review. Artif. intelligence review 56, 2923–2969 (2023)

work page 2023

[6] [6]

L., Miller, K

Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2020. CA: A Cancer J. for Clin. 70, 7–30, DOI: 10.3322/caac. 21590 (2020)

doi:10.3322/caac 2020

[7] [7]

Organization, W. H. Breast cancer. https://www.who.int/news-room/fact-sheets/detail/breast-cancer (2024). Accessed: 2024-10-10

work page 2024

[8] [8]

Lauby-Secretan, B. et al. Breast-cancer screening—viewpoint of the iarc working group. The New Engl. J. Medicine 372, 2353–2358, DOI: 10.1056/NEJMsr1504363 (2015)

work page doi:10.1056/nejmsr1504363 2015