Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder

Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutional architectures for the task of multi-class segmentation of the lungs field, heart and clavicles in a chest radiograph. In addition, we explore the influence of using different loss functions in the training process of a neural network for semantic segmentation. We evaluate all models on a common benchmark of 247 X-ray images from the JSRT database and ground-truth segmentation masks from the SCR dataset. Our best performing architecture, is a modified U-Net that benefits from pre-trained encoder weights. This model outperformed the current state-of-the-art methods tested on the same benchmark, with Jaccard overlap scores of 96.1% for lung fields, 90.6% for heart and 85.5% for clavicles.Comment: Presented at the First International Workshop on Thoracic Image Analysis (TIA), MICCAI 201

Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice

The Dice score and Jaccard index are commonly used metrics for the evaluation of segmentation tasks in medical imaging. Convolutional neural networks trained for image segmentation tasks are usually optimized for (weighted) cross-entropy. This introduces an adverse discrepancy between the learning optimization objective (the loss) and the end target metric. Recent works in computer vision have proposed soft surrogates to alleviate this discrepancy and directly optimize the desired metric, either through relaxations (soft-Dice, soft-Jaccard) or submodular optimization (Lov\'asz-softmax). The aim of this study is two-fold. First, we investigate the theoretical differences in a risk minimization framework and question the existence of a weighted cross-entropy loss with weights theoretically optimized to surrogate Dice or Jaccard. Second, we empirically investigate the behavior of the aforementioned loss functions w.r.t. evaluation with Dice score and Jaccard index on five medical segmentation tasks. Through the application of relative approximation bounds, we show that all surrogates are equivalent up to a multiplicative factor, and that no optimal weighting of cross-entropy exists to approximate Dice or Jaccard measures. We validate these findings empirically and show that, while it is important to opt for one of the target metric surrogates rather than a cross-entropy-based loss, the choice of the surrogate does not make a statistical difference on a wide range of medical segmentation tasks.Comment: MICCAI 201

Suicide in Hong Kong: A case-control psychological autopsy study

Background. The relative contribution of psychosocial and clinical risk factors to suicide among Chinese populations is an important issue. In Hong Kong, this issue requires vigorous examination in light of a 50% increase in suicide rate between 1997 and 2003. Method. Using a case-control psychological autopsy method, 150 suicide deceased were compared with 150 living controls matched by age and gender. Semi-structured interviews were conducted with the next-of-kin of the subjects. Data were collected on a wide range of potential risk and protective factors, including demographic, life event, clinical and psychological variables. The relative contribution of these factors towards suicide was examined in a multiple logistic regression model. Results. Six factors were found to significantly and independently contribute to suicide: unemployment, indebtedness, being single, social support, psychiatric illness, and history of past attempts. Conclusions. Both psychosocial and clinical factors are important in suicides in Hong Kong. They seem to have mediated suicide risk independently. In addition, socio-economic adversities seem to have played a relatively important role in the increasing suicide rate in Hong Kong. © 2006 Cambridge University Press.published_or_final_versio

Early growth response protein-1 promoter-mediated synergistic antitumor effect of hTERTC27 gene therapy and 5-flurorouracil on nasopharyngeal carcinoma

hTERTC27 is a newly constructed polypeptide that can induce telomere dysfunction. To study the synergistic antitumor effects of the hTERTC27 polypeptide driven by the early growth response protein-1 (Egr-1) promoter and chemotherapeutic 5-flurorouracil (5-FU) on nasopharyngeal carcinoma, a series of in vitro and in vivo experiments were performed. The results showed that hTERTC27 expression was significantly increased up to 7.21-folds by the 5-FU-activated Egr-1 promoter in C666-1 cells. Overexpressed hTERTC27 made the cells more sensitive to 5-FU, and additionally, inhibited cell proliferation about 20.41%. Combinational therapy of overexpressed hTERTC27 driven by the 5-FU-activated Egr-1 promoter and 5-FU synergistically inhibited cell proliferation and promoted apoptosis of C666-1 cells for about 4.75-fold and 1.76-fold in comparison with a sole therapy of hTERTC27 or 5-FU in vitro. In vivo experiments showed that overexpressed hTERTC27 driven by 5-FU-activated Egr-1 promoter and 5-FU synergistically reduced tumor volume, tumor weight, and local infiltration, which may be relative to tumor cell apoptosis. These results suggest that combinational therapy of overexpressed hTERTC27, which is driven by the 5-FU-activated Egr-1 promoter, and 5-FU may provide a novel approach to treat nasopharyngeal cancer. © 2012 Mary Ann Liebert, Inc.published_or_final_versio

Projecting the 10-year costs of care and mortality burden of depression until 2032: a Markov modelling study developed from real-world data

\ua9 2024 The Authors. Background: Based on real-world data, we developed a 10-year prediction model to estimate the burden among patients with depression from the public healthcare system payer\u27s perspective to inform early resource planning in Hong Kong. Methods: We developed a Markov cohort model with yearly cycles specifically capturing the pathway of treatment-resistant depression (TRD) and comorbidity development along the disease course. Projected from 2023 to 2032, primary outcomes included costs of all-cause and psychiatric care, and secondary outcomes were all-cause deaths, years of life lived, and quality-adjusted life-years. Using the territory-wide electronic medical records, we identified 25,190 patients aged ≥10 years with newly diagnosed depression from 2014 to 2016 with follow-up until 2020 to observe the real-world time-to-event pattern, based on which costs and time-varying transition inputs were derived using negative binomial modelling and parametric survival analysis. We applied the model as both closed cohort, which studied a fixed cohort of incident patients in 2023, and open cohort, which introduced incident patients by year from 2014 to 2032. Utilities and annual new patients were from published sources. Findings: With 9217 new patients in 2023, our closed cohort model projected the 10-year cumulative costs of all-cause and psychiatric care to reach US$309.0 million and US$58.3 million, respectively, with 899 deaths (case fatality rate: 9.8%) by 2032. In our open cohort model, 55,849–57,896 active prevalent cases would cost more than US$322.3 million and US$60.7 million, respectively, with more than 943 deaths annually from 2023 to 2032. Fewer than 20% of cases would live with TRD or comorbidities but contribute 31–54% of the costs. The greatest collective burden would occur in women aged above 40, but men aged above 65 and below 25 with medical history would have the highest costs per patient-year. The key cost drivers were relevant to the early disease stages. Interpretation: A limited proportion of patients would develop TRD and comorbidities but contribute to a high proportion of costs, which necessitates appropriate attention and resource allocation. Our projection also demonstrates the application of real-world data to model long-term costs and mortality, which aid policymakers anticipate foreseeable burden and undertake budget planning to prepare for the care need in alternative scenarios. Funding: Research Impact Fund from the University Grants Committee, Research Grants Council with matching fund from the Hong Kong Association of Pharmaceutical Industry (R7007-22)

Learning Tversky Similarity

In this paper, we advocate Tversky's ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a semantically meaningful way. We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods

Registration-Based Encryption: Removing Private-Key Generator from IBE

In this work, we introduce the notion of registration-based encryption (RBE for short) with the goal of removing the trust parties need to place in the private-key generator in an IBE scheme. In an RBE scheme, users sample their own public and secret keys. There will also be a ``key curator\u27\u27 whose job is only to aggregate the public keys of all the registered users and update the short public parameter whenever a new user joins the system. Encryption can still be performed to a particular ecipient using the recipient\u27s identity and any public parameters released subsequent to the recipient\u27s registration. Decryption requires some auxiliary information connecting users\u27 public (and secret) keys to the public parameters. Because of this, as the public parameters get updated, a decryptor may need to obtain a few additional auxiliary information for decryption. More formally, if $n$ is the total number of identities and $\kappa$ is the security parameter, we require the following. Efficiency requirements: (1) A decryptor only needs to obtain updated auxiliary information for decryption at most $O(\log n)$ times in its lifetime, (2) each of these updates are computed by the key curator in time $poly(\kappa,\log n)$, and (3) the key curator updates the public parameter upon the registration of a new party in time $poly(\kappa,\log n)$. Properties (2) and (3) require the key curator to have \emph{random} access to its data. Compactness requirements: (1) Public parameters are always at most $poly(\kappa,\log n)$ bit, and (2) the total size of updates a user ever needs for decryption is also at most $poly(\kappa,\log n)$ bits. We present feasibility results for constructions of RBE based on indistinguishably obfuscation. We further provide constructions of \emph{weakly efficient} RBE, in which the registration step is done in $poly(\kappa, n)$, based on CDH, Factoring or LWE assumptions. Note that registration is done only once per identity, and the more frequent operation of generating updates for a user, which can happen more times, still runs in time $poly(\kappa,\log n)$. We leave open the problem of obtaining standard RBE (with $poly(\kappa,\log n)$ registration time) from standard assumptions

Multi-Client Oblivious RAM with Poly-Logarithmic Communication

Oblivious RAM enables oblivious access to memory in the single-client setting, which may not be the best fit in the network setting. Multi-client oblivious RAM (MCORAM) considers a collaborative but untrusted environment, where a database owner selectively grants read access and write access to different entries of a confidential database to multiple clients. Their access pattern must remain oblivious not only to the server but also to fellow clients. This upgrade rules out many techniques for constructing ORAM, forcing us to pursue new techniques. MCORAM not only provides an alternative solution to private anonymous data access (Eurocrypt 2019) but also serves as a promising building block for equipping oblivious file systems with access control and extending other advanced cryptosystems to the multi-client setting. Despite being a powerful object, the current state-of-the-art is unsatisfactory: The only existing scheme requires $O(\sqrt n)$ communication and client computation for a database of size $n$. Whether it is possible to reduce these complexities to $\mathsf{polylog}(n)$, thereby matching the upper bounds for ORAM, is an open problem, i.e., can we enjoy access control and client-obliviousness under the same bounds? Our first result answers the above question affirmatively by giving a construction from fully homomorphic encryption (FHE). Our main technical innovation is a new technique for cross-key trial evaluation of ciphertexts. We also consider the same question in the setting with $N$ non-colluding servers, out of which at most $t$ of them can be corrupt. We build multi-server MCORAM from distributed point functions (DPF), and propose new constructions of DPF via a virtualization technique with bootstrapping, assuming the existence of homomorphic secret sharing and pseudorandom generators in NC0, which are not known to imply FHE

Subvert KEM to Break DEM: Practical Algorithm-Substitution Attacks on Public-Key Encryption

Motivated by the currently widespread concern about mass surveillance of encrypted communications, Bellare \emph{et al.} introduced at CRYPTO 2014 the notion of Algorithm-Substitution Attack (ASA) where the legitimate encryption algorithm is replaced by a subverted one that aims to undetectably exfiltrate the secret key via ciphertexts. Practically implementable ASAs on various cryptographic primitives (Bellare \emph{et al.}, CRYPTO\u2714 \& ACM CCS\u2715; Ateniese \emph{et al.}, ACM CCS\u2715; Berndt and Liśkiewicz, ACM CCS\u2717) have been constructed and analyzed, leaking the secret key successfully. Nevertheless, in spite of much progress, the practical impact of ASAs (formulated originally for symmetric key cryptography) on public-key (PKE) encryption operations remains unclear, primarily since the encryption operation of PKE does not involve the secret key, and also previously known ASAs become relatively inefficient for leaking the plaintext due to the logarithmic upper bound of exfiltration rate (Berndt and Liśkiewicz, ACM CCS\u2717). In this work, we formulate a practical ASA on PKE encryption algorithm which, perhaps surprisingly, turns out to be much more efficient and robust than existing ones, showing that ASAs on PKE schemes are far more effective and dangerous than previously believed. We mainly target PKE of hybrid encryption which is the most prevalent way to employ PKE in the literature and in practice. The main strategy of our ASA is to subvert the underlying key encapsulation mechanism (KEM) so that the session key encapsulated could be efficiently extracted, which, in turn, breaks the data encapsulation mechanism (DEM) enabling us to learn the plaintext itself. Concretely, our non-black-box yet quite general attack enables recovering the plaintext from only two successive ciphertexts and minimally depends on a short state of previous internal randomness. A widely used class of KEMs is shown to be subvertible by our powerful attack. Our attack relies on a novel identification and formalization of certain properties that yield practical ASAs on KEMs. More broadly, it points at and may shed some light on exploring structural weaknesses of other ``composed cryptographic primitives,\u27\u27 which may make them susceptible to more dangerous ASAs with effectiveness that surpasses the known logarithmic upper bound (i.e., reviewing composition as an attack enabler)