Pith. sign in

REVIEW 4 major objections 4 minor 1 cited by

Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that a 1.7M-parameter network classifies cervical cells at 91.28% accuracy, within 2-3% of much larger models, while a multi-task UNet reaches 0.83 IoU and 90% accuracy.

desk verdict A plausible lightweight classifier is buried under an unsupported prognosis claim and internally inconsistent segmentation metrics. read the letter →

arxiv 2505.15505 v1 pith:JEN3X5YC submitted 2025-05-21 eess.IV cs.CV

Abdul Samad Shaik , Shashaank Mattur Aswatha , Rahul Jashvantbhai Pandya This is my paper · ORCID
classification eess.IVcs.CV
keywords cervicalcancerPapsmeardeeplearningimagesegmentationcellclassificationriskassessmentmulti-tasklightweightCNN
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 three-stage pipeline for Pap-smear screening: segment cell boundaries in whole-slide images, classify each cell into one of five categories, and score the risk that a normal cell is progressing toward malignancy. It reports that a compact three-branch network, MRF-DCN, classifies cells at 91.28% accuracy on the SIPaKMeD dataset with only 1.7 million parameters, roughly 85 times fewer than VGG-19 and within 2-3% of much larger models. It also reports a multi-task UNet variant that segments and classifies in one pass, reaching an IoU of 0.83 and 90% classification accuracy. The last stage fits a Gaussian probability model to the learned features to produce posterior probabilities for progression, which the authors offer as a prognostic tool.

What carries the argument

The load-bearing object is MRF-DCN, a three-branch depthwise-separable convolutional network in which each branch consumes a different resolution of the same image and emits a 64-dimensional embedding; concatenation gives a 192-dimensional representation that is projected to 64 dimensions and classified by softmax. This multi-resolution design is what lets the model avoid resizing all inputs to one fixed scale and keeps the parameter count at 1.7 million. The second mechanism is the multi-task UNet, which appends a sequential classification layer to a squeezed bottleneck so segmentation and classification share one encoder and are optimized jointly. The risk score comes from a per-class multivariate Gaussian fit to the 64-dimensional features, with Bayes' theorem giving posterior probabilities and cosine similarity giving a second closeness measure.

What would settle it

Track a cohort of normal Pap smears forward in time and compare the model's risk scores with later biopsy-confirmed dysplasia or cancer; if high posterior probabilities do not predict progression, the risk-assessment claim fails. A simpler check is whether the risk score is just the classifier's confidence on a held-out set rather than a quantity tied to actual disease course.

Watch

Extended reading notes

Core claim

The authors' central claim is that all three screening tasks can be done with lightweight models without sacrificing accuracy. MRF-DCN processes each cell image at three resolutions (32x32, 64x64, 128x128) in parallel branches, fuses the three 64-dimensional embeddings into a 192-dimensional vector, and reduces it to 64 features before the final softmax; this configuration reaches 91.28% accuracy on the five SIPaKMeD classes. The multi-task UNet compresses its bottleneck and adds a classification head, training with a weighted sum of segmentation and classification losses, achieving IoU 0.83 and 90% accuracy simultaneously. The risk-assessment stage extracts the 64-dimensional features, models each class with a multivariate Gaussian, and uses Bayes' rule and cosine similarity to assign posterior probabilities that the authors interpret as the likelihood of normal cells progressing to koilocytotic or dyskaryotic states.

Load-bearing premise

The risk-assessment claim rests on the assumption that a cell's probability of belonging to a malignant class, computed from a statistical model fit to a retrospective snapshot, equals the real likelihood that the cell will progress to cancer; no follow-up data is used to test that equivalence.

Editorial extensions

If this is right

  • A model with 1.7 million parameters and 91.28% accuracy could run on far less expensive hardware than the 144-million-parameter VGG-19 baseline, making automated Pap-smear triage more feasible in low-resource settings.
  • One multi-task network can produce both a segmentation mask and a five-class label in a single pass, removing the need to chain separate segmenters and classifiers.
  • If the posterior probabilities are trustworthy, cells flagged with high risk scores could be prioritized for pathologist review, compressing the workload of screening programs.
  • The framework's segmentation-to-bounding-box step turns masks directly into classification inputs, so the pipeline can go from whole-slide image to cell-level label automatically.

Reading between the lines

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

  • The risk probabilities are class-membership scores computed on a retrospective snapshot, not transition probabilities estimated from follow-up data; reading them as true progression likelihoods would require longitudinal validation.
  • The multi-resolution fusion principle should transfer to other cytology or histology datasets with variable cell sizes and magnifications, but the paper only tests it on SIPaKMeD.
  • A testable extension would be to replace the fixed 32/64/128 resolutions with a learned scale selection, addressing the fixed-scaling limitation the authors note.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes an end-to-end pipeline for cervical cytology: cell segmentation by four standard models (UNet, LinkNet, FPN, PSPNet), classification by a new lightweight multi-resolution CNN (MRF-DCN, 1.7M parameters), and a "risk assessment" stage that attaches a probabilistic score to extracted features. The claims are: MRF-DCN achieves 91.28% accuracy on SIPaKMeD with far fewer parameters than VGG-19; a multi-task UNet reaches IoU 0.83 and 90% classification accuracy; and a Gaussian-based posterior over features predicts progression of normal cells to malignant states. The manuscript reports experiments on the SIPaKMeD dataset (and, in the conclusion, mentions Mendeley LBC) and concludes that the risk assessment can be used for prognosis.

Significance. If the claims were correct, the lightweight MRF-DCN would be a useful contribution to low-resource cervical screening, and the multi-task UNet would offer a compact unified segmentation-classification approach. The risk-assessment stage, however, is the advertised central novelty and would be clinically significant only if it actually measured progression. That claim is not demonstrated. The paper's strengths are the parameter-efficiency comparison against several standard CNNs and the explicit reporting of a multi-task loss formulation; however, the segmentation results contain a mathematical inconsistency, and the risk assessment is not validated as a longitudinal or prognostic tool.

major comments (4)
  1. [§IV-B, Table II] For binary segmentation masks, Dice and IoU are related by Dice = 2·IoU/(1+IoU). With IoU=0.81, the UNet row should report Dice ≈ 0.90, not 0.68. The same inconsistency appears for all four models: LinkNet (IoU 0.55 vs DC 0.35; expected 0.71), FPN (0.75 vs 0.63; expected 0.86), and PSPNet (0.77 vs 0.68; expected 0.87). This indicates that at least one metric is computed incorrectly or from different masks, so the segmentation comparison as presented is not internally consistent and cannot support the claim that UNet and PSPNet are the most robust segmentation models.
  2. [§IV-E, Eqs. (1)–(5) and Algorithm 1] The risk-assessment stage computes P(Ci|x) from multivariate Gaussian class-conditional densities fitted to 64-dimensional MRF-DCN features extracted from the same retrospective SIPaKMeD images. This is a soft classifier over five cross-sectional morphological categories; it answers 'which class does this feature vector resemble', not 'will this normal cell progress to malignancy'. The abstract, §I-B4, §IV-D, and §V all claim that the method 'predict[s] the likelihood of normal cells progressing to malignant states' and serves as a prognostic tool. No longitudinal follow-up, progression endpoint (e.g., CIN2+ or biopsy), external cohort, or temporal model is presented. The cosine-similarity threshold >0.65 in §IV-E is chosen post hoc from the same data and is not validated against any outcome. The high ML classification accuracies in Table VI only show that the extracted features separate the existing classes; they do not establish predictive validity for future transformation. This is a load-bearing mismatch between the advertised central contribution and what is actually computed.
  3. [§III-A] The ground-truth masks used for the segmentation experiments are never described. The text says 'Binary masks were generated for each patch' but does not state whether these masks come from manual annotation, automatic thresholding, or some other procedure. Without this information, the reported IoU/Dice values cannot be interpreted, and the bounding-box derivation from the predicted masks cannot be reproduced.
  4. [§V and §IV-E] The conclusion states that the risk-assessment method 'evaluated the SIPaKMeD dataset as well as the Mendeley LBC dataset', and §IV-E says the approach 'is effective on the Mendeley LBC Dataset ... as well'. However, no experimental results, tables, or quantitative comparisons for Mendeley LBC appear anywhere in the manuscript. This claim is unsubstantiated as written.
minor comments (4)
  1. [§IV-C, Table III] The multi-task loss in Eq. (14) depends on λ_seg and λ_cls, but Table III (the hyperparameter table for MRF-DCN) does not report these values, and the multi-task UNet section does not state its own hyperparameters or loss weights. The MTL result (IoU 0.83, accuracy 90%) is therefore not reproducible from the information given.
  2. [§IV-E, Table VI] The column headers 'M W M W M W' are undefined and should be labeled (presumably macro/weighted averages). As written, the table is difficult to interpret.
  3. [Throughout] There are inconsistencies in class terminology: 'Dyskaryotic' and 'Dyskeratotic' are used interchangeably (e.g., Fig. 7 vs Table IV), and 'begets' in the abstract is nonstandard. These should be harmonized and edited.
  4. [§II, Table I] The class labels mix normal and abnormal categories; the statement that the model was trained to 'distinguish between the four classes' in §IV-D1 is inconsistent with the five-class SIPaKMeD setup described elsewhere. Please clarify the number of classes used in each experiment.

Circularity Check

1 steps flagged · score 6.0 of 10

The risk-assessment stage equates 'likelihood of progression' with a Gaussian class-posterior computed from the same retrospective class labels, so the central prognosis claim reduces to a soft classifier.

  1. fitted input called prediction [Section III-D2 (Risk Assessment), Eqs. (1)-(5); Abstract; Section IV-E; Conclusion]
    "To measure the similarity of the sample x to the remaining classes, we normalize the likelihoods to obtain posterior probabilities that sum to one. ... In summary, our developed method offers a predictive approach to assess the probability of normal cells progressing to abnormal cells."

    Eqs. (1)-(5) define the RA output Pn(Ci|x) as the normalized Gaussian class-conditional likelihood, with mean mu_i and covariance Sigma_i computed per class from MRF-DCN features of the same SIPaKMeD images. This is a soft classifier over the five cross-sectional cytology classes; the paper itself states that these posteriors 'quantitatively measure how similar the sample is to each class.' The leap to 'probability of normal cells progressing to abnormal cells' adds a temporal/prognostic meaning that the equations do not contain. No longitudinal follow-up, progression endpoint, time variable, or external outcome cohort is used anywhere.

full rationale

The classification and segmentation contributions are self-contained and benchmarked against external baselines on SIPaKMeD: MRF-DCN accuracy and parameter counts are compared with VGG-19, ResNet, MobileNet, etc., and the MTL UNet is evaluated with IoU and accuracy. These claims are not circular. The circularity is confined to the risk-assessment / prognosis claim, which is nevertheless advertised as a central contribution in the abstract, contribution list, framework description, and conclusion. The paper computes posterior probabilities of the five SIPaKMeD classes from Gaussian distributions fitted to features of the same retrospective images and then calls these posteriors the likelihood of normal cells progressing to malignant states. Since the class labels are simultaneous morphological categories in a cross-sectional dataset, the computed quantity is a soft classification score, not a progression probability. The paper's own text concedes that the posteriors measure similarity to classes, and no longitudinal or external validation is presented. Thus the central RA 'prediction' reduces by construction to the fitted classifier output, giving a partial but significant circularity score of 6. The unsupported threshold cosine similarity > 0.65 is a related overreach but is secondary to the definitional reduction.

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

The central claims rest on standard math (Gaussian densities, Bayes), domain assumptions about the SIPaKMeD labels and the self-generated segmentation masks, and two ad hoc assumptions in the RA stage: Gaussian feature distributions and the semantic equation of class posteriors with progression risk. The MRF-DCN architecture itself introduces no new axioms, only hand-chosen hyperparameters.

free parameters (4)
  • MRF-DCN input resolutions = 32x32, 64x64, 128x128
    Chosen by hand for the three branches; no ablation shows these sizes are optimal or that multi-resolution input is necessary.
  • MTL loss weights (lambda_seg, lambda_cls) = not reported
    Eq. 14 defines the combined loss but the numerical weights are omitted, so the reported IoU 0.83 and accuracy 90% cannot be reproduced.
  • Cosine similarity threshold for progression risk = 0.65
    Set post hoc in Section IV-E ('a cosine similarity greater than 0.65 indicates a high probability of progression'); no threshold optimization or validation on independent data.
  • Bounding box padding offset O = not specified
    Introduced in Section III-A to prevent truncation of the box; the value of O is never given.
assumptions (4)
  • domain assumption The binary masks used to train segmentation networks are correct representations of cell boundaries.
    Section III-A states masks were generated with white for cell regions and black for background, but the procedure (manual annotation, thresholding, or algorithm) is not described and no validation is shown.
  • domain assumption SIPaKMeD class labels are reliable ground truth for the five cell categories.
    The entire classification and RA evaluation treats SIPaKMeD labels as gold standard, which is standard but an unverified assumption in this work.
  • ad hoc to paper Feature vectors in each class follow a multivariate Gaussian distribution (Eq. 1).
    The RA method assumes Gaussian class-conditional densities without testing normality or accounting for the high dimensionality relative to class sample size.
  • ad hoc to paper The posterior probability of belonging to an abnormal class is equivalent to the likelihood of progression from normal to malignant cells.
    Section IV-E and the conclusion interpret class posteriors as progression risk; this semantic step has no longitudinal or biological justification.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer." pith.science (2026). https://pith.science/paper/JEN3X5YC

@misc{pith2026250515505,
  author       = {Pith},
  title        = {Pith review of: Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JEN3X5YC}},
  note         = {Machine review of arXiv:2505.15505}
}
read the original abstract

Cervical cancer, the fourth leading cause of cancer in women globally, requires early detection through Pap smear tests to identify precancerous changes and prevent disease progression. In this study, we performed a focused analysis by segmenting the cellular boundaries and drawing bounding boxes to isolate the cancer cells. A novel Deep Learning (DL) architecture, the ``Multi-Resolution Fusion Deep Convolutional Network", was proposed to effectively handle images with varying resolutions and aspect ratios, with its efficacy showcased using the SIPaKMeD dataset. The performance of this DL model was observed to be similar to the state-of-the-art models, with accuracy variations of a mere 2\% to 3\%, achieved using just 1.7 million learnable parameters, which is approximately 85 times less than the VGG-19 model. Furthermore, we introduced a multi-task learning technique that simultaneously performs segmentation and classification tasks and begets an Intersection over Union score of 0.83 and a classification accuracy of 90\%. The final stage of the workflow employs a probabilistic approach for risk assessment, extracting feature vectors to predict the likelihood of normal cells progressing to malignant states, which can be utilized for the prognosis of cervical cancer.

Figures

Figures reproduced from arXiv: 2505.15505 by the authors.

Figure 1
Figure 1. Proposed framework for cervical cancer diagnosis [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Examples of WSI (a) (b) (c) (d) (e) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Examples of cell patches: (a) Dyskaryotic (b) Koilocy [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Multi-Resolution Fusion in Deep Convolutional Network (MRF-DCN) [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Multi-task UNet with a squeezed bottomnecklayer [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Proposed Approach for Risk Assessment The class into which the sample x is assigned has the highest posterior probability P(Ci |x) as shown in Equation 4, where arg max function identifies the index i of the class Ci that maximizes the posterior probability P(Ci |x). I…
Figure 7
Figure 7. Figure 7: Confusion matrix illustrating the performance of MRF [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: MRF-DCN: CE Loss [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: MRF-DCN Accuracy [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: ROC curves for various DL models evaluated on test set. [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]

Discussion (0). Continue with ORCID to comment.

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

  1. Geometry-aware Gaussian Prior and Axial Attention for Cervical Cytology Image Classification

    cs.CV 2026-07 conditional novelty 5.5 of 10

    Geometry-aware Gaussian expert priors injected into axial self-attention improve cervical cytology classification to 99.48% and 96.08% accuracy on two public datasets.

Reference graph

Works this paper leans on

40 extracted references · 37 canonical work pages · cited by 1 Pith paper

  1. [1]

    Cervical cancer: Prevention and early detection,

    T. A. Kessler, “Cervical cancer: Prevention and early detection,” Semin. Oncol. Nurs., vol. 33, no. 2, pp. 172–183, May 2017

    work page 2017

  2. [2]

    Cervical cancer diagnosis using random forest classifier with smote and feature reduction techniques,

    S. F. Abdoh, M. Abo Rizka, and F. A. Maghraby, “Cervical cancer diagnosis using random forest classifier with smote and feature reduction techniques,” IEEE Access, vol. 6, pp. 59 475–59 485, Oct. 2018

    work page 2018

  3. [3]

    Principal components analysis (PCA),

    A. Ma ´ckiewicz and W. Ratajczak, “Principal components analysis (PCA),” Comput. Geosci., vol. 19, no. 3, pp. 303–342, Mar. 1993

    work page 1993

  4. [4]

    A review of applications of image analysis and machine learning techniques in automated diagnosis and classification of cervical cancer from pap-smear images,

    W. William, A. H. Basaza-Ejiri, J. Obungoloch, and A. Ware, “A review of applications of image analysis and machine learning techniques in automated diagnosis and classification of cervical cancer from pap-smear images,” in 2018 IST-Africa Week Conference (IST-Africa) , July 2018, pp. 1–11

    work page 2018

  5. [5]

    Historical analysis of the brazilian cervical cancer screening program from 2006 to 2013: A time for reflection,

    R. F. A. Costa, A. Longatto-Filho, C. Pinheiro, L. C. Zeferino, and J. H. Fregnani, “Historical analysis of the brazilian cervical cancer screening program from 2006 to 2013: A time for reflection,” PLoS One, vol. 10, no. 9, pp. 1–11, Sep. 2015

    work page 2006

  6. [6]

    Segmentation and clas- sification techniques for pap smear images in detecting cervical cancer: A systematic review,

    B. Z. Wubineh, A. Rusiecki, and K. Halawa, “Segmentation and clas- sification techniques for pap smear images in detecting cervical cancer: A systematic review,” IEEE Access, vol. 12, pp. 118 195–118 213, Aug. 2024

    work page 2024

  7. [7]

    U-Net: Convolutional net- works for biomedical image segmentation,

    O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional net- works for biomedical image segmentation,” pp. 234–241, May 2015

    work page 2015

  8. [8]

    A survey of convolutional neural networks: Analysis, applications, and prospects,

    Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A survey of convolutional neural networks: Analysis, applications, and prospects,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 33, no. 12, pp. 6999–7019, Dec. 2022

    work page 2022

Show all 40 references
  1. [9]

    Early detection and categorization of cervical cancer cells using smoothing cross entropy-based multi-deep transfer learning,

    R. Ahmed, N. Dahmani, G. Dahy, A. Darwish, and A. Ella Has- sanien, “Early detection and categorization of cervical cancer cells using smoothing cross entropy-based multi-deep transfer learning,”IEEE Access, vol. 12, pp. 157 838–157 853, Oct. 2024. 11

    2024

  2. [10]

    Searching for mobilenetv3,

    A. Howard, M. Sandler, B. Chen, W. Wang, L.-C. Chen, M. Tan, G. Chu, V . Vasudevan, Y . Zhu, R. Pang, H. Adam, and Q. Le, “Searching for mobilenetv3,” in Proc. IEEE Int. Conf. Comput. Vis., Los Alamitos, CA, USA, Nov. 2019, pp. 1314–1324

    2019

  3. [11]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Dec. 2016, pp. 770–778

    2016

  4. [12]

    MLNet: metaheuristics- based lightweight deep learning network for cervical cancer diagnosis,

    M. Kaur, D. Singh, V . Kumar, and H.-N. Lee, “MLNet: metaheuristics- based lightweight deep learning network for cervical cancer diagnosis,” IEEE J. Biomed. Health Informat. , vol. 27, no. 10, pp. 5004–5014, Oct. 2023

    2023

  5. [13]

    Pap-smear (DTU/HERLEV) databases,

    J. Jantzen, “Pap-smear (DTU/HERLEV) databases,” Accessed: Dec. 17, 2021, 2008. [Online]. Available: mde-lab.aegean.gr/downloads

    2021

  6. [14]

    Sipakmed: A new dataset for feature and image based classification of normal and pathological cervical cells in pap smear images,

    M. E. Plissiti, P. Dimitrakopoulos, G. Sfikas, C. Nikou, O. Krikoni, and A. Charchanti, “Sipakmed: A new dataset for feature and image based classification of normal and pathological cervical cells in pap smear images,” in 2018 25th Proc. - Int. Conf. Image Process. ICIP , Sep...

    2018

  7. [15]

    Liquid based- cytology pap smear dataset for automated multi-class diagnosis of pre- cancerous and cervical cancer lesions,

    E. Hussain, L. B. Mahanta, H. Borah, and C. R. Das, “Liquid based- cytology pap smear dataset for automated multi-class diagnosis of pre- cancerous and cervical cancer lesions,” p. 105589, June 2020

    2020

  8. [16]

    Deeppap: Deep convolutional networks for cervical cell classification,

    L. Zhang, L. Lu, I. Nogues, R. M. Summers, S. Liu, and J. Yao, “Deeppap: Deep convolutional networks for cervical cell classification,” IEEE Journal of Biomedical and Health Informatics , vol. 21, no. 6, pp. 1633–1643, May 2017

    2017

  9. [17]

    A deep neural network for cervical cell classification based on cytology images,

    M. Fang, X. Lei, B. Liao, and F.-X. Wu, “A deep neural network for cervical cell classification based on cytology images,” IEEE Access , vol. 10, pp. 130 968–130 980, Jan. 2022

    2022

  10. [18]

    Res dcgan and resnet50v2 with self-attention for cervical cell classification,

    S. Khan, J. Ahmad, and S. Hussain, “Res dcgan and resnet50v2 with self-attention for cervical cell classification,” Neural Comput. Appl. , vol. 36, no. 1, pp. 123–138, Sept. 2024

    2024

  11. [19]

    Ps3c: A two-step framework for pap smear screening support with deep ensemble learning,

    S. Di Piazza and H. Boussel, “Ps3c: A two-step framework for pap smear screening support with deep ensemble learning,” Biomed. Signal Process. Control., vol. 90, p. 105014, Mar. 2025

    2025

  12. [20]

    Evaluation of deep transfer learning for cervical cancer classification using pap smear images,

    Y . Zhang, R. Kumar, and H. Lee, “Evaluation of deep transfer learning for cervical cancer classification using pap smear images,” Comput. Biol. Med., vol. 165, p. 107480, Jan. 2025

    2025

  13. [21]

    Application of deep learning algorithm in cervical cancer mri image segmentation based on wireless sensor,

    P. Liang, G. Sun, and S. Wei, “Application of deep learning algorithm in cervical cancer mri image segmentation based on wireless sensor,” J. Med. Syst., vol. 43, Apr. 2019

    2019

  14. [22]

    Deep learning for cell image segmentation and ranking,

    F. H. Ara ´ujo, R. R. Silva, D. M. Ushizima, M. T. Rezende, C. M. Carneiro, A. G. Campos Bianchi, and F. N. Medeiros, “Deep learning for cell image segmentation and ranking,” Comput. Med. Imaging Graph. , vol. 72, pp. 13–21, Mar. 2019

    2019

  15. [23]

    A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei,

    Y . Song, L. Zhang, S. Chen, D. Ni, B. Li, Y . Zhou, B. Lei, and T. Wang, “A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei,” in 2014 36th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., Nov. 2014, pp. 2903–2906

    2014

  16. [24]

    Automatic segmentation of cervical nuclei based on deep learning and a conditional random field,

    Y . Liu, P. Zhang, Q. Song, A. Li, P. Zhang, and Z. Gui, “Automatic segmentation of cervical nuclei based on deep learning and a conditional random field,” IEEE Access, vol. 6, pp. 53 709–53 721, Sep. 2018

    2018

  17. [25]

    Segmentation of cervical cell images based on generative adversarial networks,

    J. Huang, G. Yang, B. Li, Y . He, and Y . Liang, “Segmentation of cervical cell images based on generative adversarial networks,” IEEE Access , vol. 9, pp. 115 415–115 428, Aug. 2021

    2021

  18. [26]

    Accurate cervical cell segmentation from overlapping clumps in pap smear images,

    Y . Song, E.-L. Tan, X. Jiang, J.-Z. Cheng, D. Ni, S. Chen, B. Lei, and T. Wang, “Accurate cervical cell segmentation from overlapping clumps in pap smear images,” IEEE Trans. Med. Imaging. , vol. 36, no. 1, pp. 288–300, Jan. 2017

    2017

  19. [27]

    A framework for diagnosing cervical cancer disease based on feedforward MLP neural network and thinprep histopathological cell image features,

    B. Sokouti, S. Haghipour, and A. Dastranj Tabrizi, “A framework for diagnosing cervical cancer disease based on feedforward MLP neural network and thinprep histopathological cell image features,” Neural Comput. Appl., vol. 24, Jan. 2014

    2014

  20. [28]

    Automatic classification of cervical cancer from cytological images by using convolutional neural network,

    M. Wu, C. Yan, H. Liu, Q. Liu, and Y . Yin, “Automatic classification of cervical cancer from cytological images by using convolutional neural network,” Biosci. Rep., vol. 38, p. BSR20181769, Oct. 2018

    2018

  21. [29]

    Automatic cervical cell segmentation and classification in pap smears,

    T. Chankong, N. Theera-Umpon, and S. Auephanwiriyakul, “Automatic cervical cell segmentation and classification in pap smears,” Comput. Methods Programs Biomed. , vol. 113, no. 2, pp. 539–556, Feb. 2014

    2014

  22. [30]

    Cytoplasm and nucleus segmentation in cervical smear images using radiating GVF snake,

    K. Li, Z. Lu, W. Liu, and J. Yin, “Cytoplasm and nucleus segmentation in cervical smear images using radiating GVF snake,” Pattern Recognit., vol. 45, no. 4, pp. 1255–1264, Apr. 2012

    2012

  23. [31]

    Nucleus and cytoplast contour detector of cervical smear image,

    P. Y . Pai, C. C. Chang, and Y . K. Chan, “Nucleus and cytoplast contour detector of cervical smear image,” Expert Syst. Appl. , vol. 39, pp. 154– 161, Jan. 2012

    2012

  24. [32]

    Nucleus and cytoplast contour detector of cervical smear image,

    M. H. Tsai, Y . K. Chan, Z. Z. Lin, S. F. Yang-Mao, and P. C. Huang, “Nucleus and cytoplast contour detector of cervical smear image,” Pattern Recognit. Lett., vol. 29, no. 9, pp. 1441–1453, July 2008

    2008

  25. [33]

    LinkNet: exploiting encoder repre- sentations for efficient semantic segmentation,

    A. Chaurasia and E. Culurciello, “LinkNet: exploiting encoder repre- sentations for efficient semantic segmentation,” in Vis. Commun. Image Process. IEEE, Dec. 2017, pp. 1–4

    2017

  26. [34]

    Feature pyramid networks for object detection,

    T.-Y . Lin, P. Doll ´ar, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie, “Feature pyramid networks for object detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. , pp. 936–944, Dec. 2016

    2016

  27. [35]

    Pyramid scene parsing network,

    H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Nov. 2017, pp. 6230–6239

    2017

  28. [36]

    Segmentation models,

    P. Iakubovskii, “Segmentation models,” 2019. [Online]. Available: https://github.com/qubvel/segmentation models

    2019

  29. [37]

    Imagenet classifica- tion with deep convolutional neural networks,

    A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classifica- tion with deep convolutional neural networks,” in Advances in Neural Information Processing Systems , F. Pereira, C. Burges, L. Bottou, and K. Weinberger, Eds., vol. 25. Curran Associates, Inc., 2012

    2012

  30. [38]

    Densely connected convolutional networks,

    Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q. , “Densely connected convolutional networks,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Los Alamitos, CA, USA: IEEE Computer Society, Jul. 2017, pp. 2261–2269

    2017

  31. [39]

    Very deep convolutional networks for large-scale image recognition,

    K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR, vol. abs/1409.1556, Sept. 2014

    2014 arXiv

  32. [40]

    Adam: A method for stochastic optimization,

    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” CoRR, vol. abs/1412.6980, Dec. 2014

    2014 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.