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PathoSafe is an 816-byte int8-projection low-rank Mahalanobis out-of-distribution (OOD) head reading a frozen detector\u2019s penultimate feature. It reaches a cross-site area under the receiver operating characteristic curve (AUROC) of 0.980 and multi-source AUROC of 0.964 (parasitic-egg-dominated), within 0.01 of a 16,896-byte full-precision dense baseline, and adds a measured 30.76 \u00b5s and 816 bytes of read-only memory (ROM) constants on an STM32H743 development board, so abstention adds only a small measured cost on-chip. The advantage is the trunk feature rather than the integer form. Certification is the harder problem and carries our central result: slide clustering silently breaks the standard independent-sample certificate, since distribution-free risk control assumes exchangeable samples that patch-clustered medical data do not supply. On a degraded-input risk certificate, the naive cell-level version holds on only 20% of leakage-free re-splits, whereas the correct slide-level one is valid at 0.266 with its width set by the slide count rather than by any bound we evaluate. The deployed threshold inherits this less severely. A Hoeffding\u2013Bentkus budget gives an illustrative count of 38 independent slides under the stated assumptions (35 when five seeds are used), which is about 1.3\u00d7 what this benchmark provides. We release that leakage-free slide-disjoint benchmark: an in-distribution malaria task with cross-site (BBBC041) and multi-source (SIPaKMeD, parasitic egg, white blood cells) regimes.<\/jats:p>","DOI":"10.3390\/bios16090518","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T11:41:35Z","timestamp":1789645295000},"page":"518","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PathoSafe: Sub-1 KB Out-of-Distribution Rejection and Cluster-Aware Certification for Point-of-Care Pathogen Biosensing"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4511-5055","authenticated-orcid":false,"given":"Gaoming","family":"He","sequence":"first","affiliation":[{"name":"Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou 543000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3199-6312","authenticated-orcid":false,"given":"Mengdi","family":"Hou","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou 543000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3549-9036","authenticated-orcid":false,"given":"Jianbo","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Application, Guilin University of Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Longquan","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou 543000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Qihuang","family":"Gao","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou 543000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Bitie","family":"Lan","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou 543000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2024). World Malaria Report 2024, World Health Organization. Technical report."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hou, M., He, G., Liu, Z., Huang, J., and Zou, H. (2026). MalariaNet: A Microcontroller-Deployable Malaria-Microscopy Detector for Point-of-Care Biosensing under Leakage-Free Evaluation. Biosensors, 16.","DOI":"10.20944\/preprints202606.0677.v1"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nettur, S.B., Karpurapu, S., Nettur, U., Gajja, L.S., Myneni, S., Dusi, A., and Posham, L. (2025). UltraLightSqueezeNet: A deep learning architecture for malaria classification with up to 54x fewer trainable parameters for resource constrained devices. arXiv.","DOI":"10.1109\/ACCESS.2025.3571696"},{"key":"ref_4","unstructured":"Guo, C., Pleiss, G., Sun, Y., and Weinberger, K.Q. (2017, January 6\u201311). On calibration of modern neural networks. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.trsl.2017.12.004","article-title":"Image analysis and machine learning for detecting malaria","volume":"194","author":"Poostchi","year":"2018","journal-title":"Transl. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13249","DOI":"10.1038\/s41598-024-63831-0","article-title":"Efficient deep learning-based approach for malaria detection using red blood cell smears","volume":"14","author":"Mujahid","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_7","unstructured":"Quinn, J.A., Nakasi, R., Mugagga, P.K., Byanyima, P., Lubega, W., and Andama, A. (2016). Deep convolutional neural networks for microscopy-based point of care diagnostics. Proceedings of the Machine Learning for Healthcare Conference, PMLR."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Obuchowicz, R., Strzelecki, M., and Pi\u00f3rkowski, A. (2024). Clinical applications of artificial intelligence in medical imaging and image processing. Cancers, 16.","DOI":"10.3390\/books978-3-7258-1260-8"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fuhad, K.F., Tuba, J.F., Sarker, M.R.A., Momen, S., Mohammed, N., and Rahman, T. (2020). Deep learning based automatic malaria parasite detection from blood smear and its smartphone based application. Diagnostics, 10.","DOI":"10.3390\/diagnostics10050329"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5635","DOI":"10.1007\/s11263-024-02117-4","article-title":"Generalized out-of-distribution detection: A survey","volume":"132","author":"Yang","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"ref_11","unstructured":"Hendrycks, D., and Gimpel, K. (2017, January 24\u201326). A baseline for detecting misclassified and out-of-distribution examples in neural networks. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_12","unstructured":"Liu, W., Wang, X., Owens, J.D., and Li, Y. (2020, January 6\u201312). Energy-based out-of-distribution detection. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Virtual."},{"key":"ref_13","unstructured":"Lee, K., Lee, K., Lee, H., and Shin, J. (2018, January 3\u20138). A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Montr\u00e9al, QC, Canada."},{"key":"ref_14","unstructured":"Sun, Y., Ming, Y., Zhu, X., and Li, Y. (2022, January 17\u201323). Out-of-distribution detection with deep nearest neighbors. Proceedings of the International Conference on Machine Learning (ICML), Baltimore, MD, USA."},{"key":"ref_15","unstructured":"Tayebati, S., Kumar, D., Darabi, N., Jayasuriya, D., Krishnan, R., and Trivedi, A.R. (2025). Learning conformal abstention policies for adaptive risk management in large language and vision-language models. arXiv."},{"key":"ref_16","first-page":"78","article-title":"Are We Ready for Out-of-Distribution Detection in Digital Pathology?","volume":"Volume 15010","author":"Oh","year":"2024","journal-title":"Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI)"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gutbrod, M., Rauber, D., Nunes, D.W., and Palm, C. (2025, January 10\u201317). OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.02410"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dave, I.R., de Blegiers, T., Chen, C., and Shah, M. (2024, January 27\u201330). CodaMal: Contrastive domain adaptation for malaria detection in low-cost microscopes. Proceedings of the IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/ICIP51287.2024.10647834"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guillon, L., Biga, S., Kantchire, Y.E., Sane, M.L., Pasquier, G., Yakpa, K., Sossou, S.E., Thellier, M., Bonnardot, L., and Lachaud, L. (2025). Towards field-ready AI-based malaria diagnosis: A continual learning approach. arXiv.","DOI":"10.1007\/978-3-032-09569-5_17"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tsutsui, S., Su, Z., and Wen, B. (2023, January 4\u201310). Benchmarking white blood cell classification under domain shift. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10097167"},{"key":"ref_21","unstructured":"Vovk, V., Gammerman, A., and Shafer, G. (2005). Algorithmic Learning in a Random World, Springer."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1561\/2200000101","article-title":"Conformal prediction: A gentle introduction","volume":"16","author":"Angelopoulos","year":"2023","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3478535","article-title":"Distribution-free, risk-controlling prediction sets","volume":"68","author":"Bates","year":"2021","journal-title":"J. ACM"},{"key":"ref_24","unstructured":"Angelopoulos, A.N., Bates, S., Cand\u00e8s, E.J., Jordan, M.I., and Lei, L. (2021). Learn then test: Calibrating predictive algorithms to achieve risk control. arXiv."},{"key":"ref_25","unstructured":"Angelopoulos, A.N., Bates, S., Fisch, A., Lei, L., and Schuster, T. (2024, January 7\u201311). Conformal risk control. Proceedings of the International Conference on Learning Representations (ICLR), Vienna, Austria."},{"key":"ref_26","unstructured":"Xu, Y., Guo, W., and Wei, Z. (2025). Selective conformal risk control. arXiv."},{"key":"ref_27","unstructured":"Tibshirani, R.J., Barber, R.F., Cand\u00e8s, E.J., and Ramdas, A. (2019, January 8\u201314). Conformal prediction under covariate shift. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1214\/23-AOS2276","article-title":"Conformal prediction beyond exchangeability","volume":"51","author":"Barber","year":"2023","journal-title":"Ann. Stat."},{"key":"ref_29","unstructured":"Ren, J., Fort, S., Liu, J., Roy, A.G., Padhy, S., and Lakshminarayanan, B. (2021). A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. arXiv."},{"key":"ref_30","unstructured":"Yang, J., Wang, P., Zou, D., Zhou, Z., Ding, K., Peng, W., Wang, H., Chen, G., Li, B., and Sun, Y. (December, January 28). OpenOOD: Benchmarking Generalized Out-of-Distribution Detection. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, New Orleans, LA, USA."},{"key":"ref_31","unstructured":"Sun, Y., Guo, C., and Li, Y. (2021, January 6\u201314). ReAct: Out-of-distribution Detection With Rectified Activations. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Virtual."},{"key":"ref_32","unstructured":"Djurisic, A., Bozanic, N., Ashok, A., and Liu, R. (2023, January 1\u20135). Extremely Simple Activation Shaping for Out-of-Distribution Detection. Proceedings of the International Conference on Learning Representations (ICLR), Kigali, Rwanda."},{"key":"ref_33","unstructured":"Liang, S., Li, Y., and Srikant, R. (May, January 30). Enhancing the Reliability of Out-of-distribution Image Detection in Neural Networks. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, Z., Feng, L., and Zhang, W. (2022, January 19\u201324). ViM: Out-of-Distribution With Virtual-Logit Matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00487"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Park, J., Jung, Y.G., and Teoh, A.B.J. (2023, January 2\u20136). Nearest Neighbor Guidance for Out-of-Distribution Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00162"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e4568","DOI":"10.7717\/peerj.4568","article-title":"Pre-Trained Convolutional Neural Networks as Feature Extractors Toward Improved Malaria Parasite Detection in Thin Blood Smear Images","volume":"6","author":"Rajaraman","year":"2018","journal-title":"PeerJ"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1038\/nmeth.2083","article-title":"Annotated High-Throughput Microscopy Image Sets for Validation","volume":"9","author":"Ljosa","year":"2012","journal-title":"Nat. Methods"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Plissiti, M.E., Dimitrakopoulos, P., Sfikas, G., Nikou, C., Krikoni, O., and Charchanti, A. (2018, January 7\u201310). SIPAKMED: A New Dataset for Feature and Image Based Classification of Normal and Pathological Cervical Cells in Pap Smear Images. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451588"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Anantrasirichai, N., Chalidabhongse, T.H., Palasuwan, D., Naruenatthanaset, K., Kobchaisawat, T., Nunthanasup, N., Boonpeng, K., Ma, X., and Achim, A. (2022, January 16\u201319). ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France.","DOI":"10.1109\/ICIP46576.2022.9897267"},{"key":"ref_40","unstructured":"Maurer, A., and Pontil, M. (2009, January 18\u201321). Empirical Bernstein bounds and sample variance penalization. Proceedings of the Conference on Learning Theory (COLT), Montr\u00e9al, QC, Canada."},{"key":"ref_41","unstructured":"Lai, L., Suda, N., and Chandra, V. (2018). CMSIS-NN: Efficient neural network kernels for Arm Cortex-M CPUs. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","article-title":"A dataset for breast cancer histopathological image classification","volume":"63","author":"Spanhol","year":"2016","journal-title":"IEEE Trans. Biomed. 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