UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs

Concept-based explanations for convolutional neural networks (CNNs) aim to explain model behavior and outputs using a pre-defined set of semantic concepts (e.g., the model recognizes scene class ``bedroom'' based on the presence of concepts ``bed'' and ``pillow''). However, they often do not faithfully (i.e., accurately) characterize the model's behavior and can be too complex for people to understand. Further, little is known about how faithful and understandable different explanation methods are, and how to control these two properties. In this work, we propose UFO, a unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations. UFO formalizes understandability and faithfulness as mathematical objectives and unifies most existing concept-based explanations methods for CNNs. Using UFO, we systematically investigate how explanations change as we turn the knobs of faithfulness and understandability. Our experiments demonstrate a faithfulness-vs-understandability tradeoff: increasing understandability reduces faithfulness. We also provide insights into the ``disagreement problem'' in explainable machine learning, by analyzing when and how concept-based explanations disagree with each other

Gender Artifacts in Visual Datasets

Gender biases are known to exist within large-scale visual datasets and can be reflected or even amplified in downstream models. Many prior works have proposed methods for mitigating gender biases, often by attempting to remove gender expression information from images. To understand the feasibility and practicality of these approaches, we investigate what $\textit{gender artifacts}$ exist within large-scale visual datasets. We define a $\textit{gender artifact}$ as a visual cue that is correlated with gender, focusing specifically on those cues that are learnable by a modern image classifier and have an interpretable human corollary. Through our analyses, we find that gender artifacts are ubiquitous in the COCO and OpenImages datasets, occurring everywhere from low-level information (e.g., the mean value of the color channels) to the higher-level composition of the image (e.g., pose and location of people). Given the prevalence of gender artifacts, we claim that attempts to remove gender artifacts from such datasets are largely infeasible. Instead, the responsibility lies with researchers and practitioners to be aware that the distribution of images within datasets is highly gendered and hence develop methods which are robust to these distributional shifts across groups.Comment: ICCV 202

Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset

Current dataset collection methods typically scrape large amounts of data from the web. While this technique is extremely scalable, data collected in this way tends to reinforce stereotypical biases, can contain personally identifiable information, and typically originates from Europe and North America. In this work, we rethink the dataset collection paradigm and introduce GeoDE, a geographically diverse dataset with 61,940 images from 40 classes and 6 world regions, and no personally identifiable information, collected through crowd-sourcing. We analyse GeoDE to understand differences in images collected in this manner compared to web-scraping. Despite the smaller size of this dataset, we demonstrate its use as both an evaluation and training dataset, highlight shortcomings in current models, as well as show improved performances when even small amounts of GeoDE (1000 - 2000 images per region) are added to a training dataset. We release the full dataset and code at https://geodiverse-data-collection.cs.princeton.edu

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Tackling Bias within Computer Vision Models

Over the past decade the rapid increase in the ability of computer vision models has led to their applications in a variety of real-world applications from self-driving cars to medical diagnoses. However, there is increasing concern about the fairness and transparency of these models. In this thesis, we tackle these issue of bias within these models along two different axes. First, we consider the datasets that these models are trained on. We use two different methods to create a more balanced training dataset. First, we create a synthetic balanced dataset by sampling strategically from the latent space of a generative network. Next, we explore the potential of creating a dataset through a method other than scraping the internet: we solicit images from workers around the world, creating a dataset that is balanced across different geographical regions. Both techniques are shown to help create models with less bias. Second, we consider methods to improve interpretability of these models, which can then reveal potential biases within the model. We investigate a class of interpretability methods called concept-based methods that output explanations for models in terms of human understandable semantic concepts. We demonstrate the need for more careful development of the datasets used to learn the explanation as well as the concepts used within these explanations. We construct a new method that allows for users to select a trade-off between the understandability and faithfulness of the explanation. Finally, we discuss how methods that completely explain a model can be developed, and provide heuristics for the same

Interesting and unusual clinical presentations in leprosy at a referral center

Background: Leprosy is a disease of declining global endemicity but is still an important health-care problem in India. Pure neural leprosy is an important subset of presentations of leprosy in India. Leprosy is a known disease of the skin and nerves, but cases of pure neural involvement are relatively less. We hereby present 10 cases of pure neural leprosy in which the diagnosis of leprosy was difficult with routine methods. Materials and Methods: The study was conducted at the main referral center and satellite clinics of our organization. A retrospective analysis of patient records for the last four years was undertaken to identify patients presenting with predominantly neurological manifestations and uncommon presentations including those without skin lesions. The medical records of the patients were used as source of data. All the patients were subjected to a detailed clinical examination and bacteriological examination with slit-skin smears. Investigations like nerve biopsy, electromyography, and nerve conduction studies were done in patients with diagnostic difficulties. Results: Patients presented with neurological symptoms like paresthesias (60%), diminished sensations (40%), nonhealing ulcers (30%), and blisters (20%). All except one had thickened nerves on clinical examination. Slit-skin smear was negative in all but one patient. Nerve biopsy confirmed the diagnosis of leprosy in seven cases. Conclusion: Pure neural leprosy is difficult to diagnose with routine methods. The diagnosis should be considered, especially by neurologists and dermatologists, who are more likely to see such patients with predominant neural manifestations. The diagnosis should be confirmed with nerve biopsy to prevent delay in therapy and associated complications

Empowerment of women and mental health promotion: a qualitative study in rural Maharashtra, India.

BACKGROUND: The global burden of mental illness is high and opportunities for promoting mental health are neglected in most parts of the world. Many people affected by mental illness live in developing countries, where treatment and care options are limited. In this context, primary health care (PHC) programs can indirectly promote mental health by addressing its determinants i.e. by enhancing social unity, minimising discrimination and generating income opportunities. The objectives of this study were to: 1. Describe concepts of mental health and beliefs about determinants of mental health and illness among women involved with a PHC project in rural Maharashtra, India; 2. Identify perceived mental health problems in this community, specifically depression, suicide and violence, their perceived causes, and existing and potential community strategies to respond to them and; 3. Investigate the impact of the PHC program on individual and community factors associated with mental health METHOD: We undertook qualitative in-depth interviews with 32 women associated with the PHC project regarding: their concepts of mental health and its determinants; suicide, depression and violence; and the perceived impact of the PHC project on the determinants of mental health. The interviews were taped, transcribed, translated and thematically analysed. RESULTS: Mental health and illness were understood by these women to be the product of cultural and socio-economic factors. Mental health was commonly conceptualised as an absence of stress and the commonest stressors were conflict with husbands and mother-in-laws, domestic violence and poverty. Links between empowerment of women through income generation and education, reduction of discrimination based on caste and sex, and promotion of individual and community mental health were recognised. However, mental health problems such as suicide and violence were well-described by participants. CONCLUSION: While it is essential that affordable, accessible, appropriate treatments and systems of referral and care are available for people with mental illness in developing country settings, the promotion of mental health by addressing its determinants is another potential strategy for reducing the burden of mental illness for individuals and communities in these settings

An Analysis of Tolerance and Early Survival Outcomes with Perioperative Modified FLOT in Gastric Cancers

Abstract Anant Ramaswamy Purpose Perioperative chemotherapy with fluorouracil plus leucovorin, oxaliplatin, and docetaxel (FLOT) is a current standard of care for locoregionally advanced gastric adenocarcinomas. There is limited real world data with regard to the tolerance and efficacy of this regimen. Materials and Methods This is a retrospective analysis of gastric cancer patients who were offered neoadjuvant perioperative modified FLOT regimen between December 2016 and October 2018, at the Tata Memorial Hospital, Mumbai. Chemotherapy-related side-effects are reported along with overall survival (OS), as calculated by Kaplan-Meier method. Results Three hundred and forty-three consecutive patients were started on neoadjuvant chemotherapy (NACT) with mFLOT of which 298 patients (87%) completed the planned treatment. A total of 294 patients (86%) underwent curative resection of gastric cancer. Common grade 3 and grade 4 toxicities during NACT were diarrhea in 42 patients (12%) and febrile neutropenia in 27 patients (8%). Toxic death was seen in nine (2.6%) patients. A total of 264 patients (77%) completed planned adjuvant chemotherapy. Common grade 3 and grade 4 toxicities during adjuvant therapy were diarrhea in 42 patients (12%) and febrile neutropenia in 16 patients (6%). With a median follow-up of 19 months, the estimated 2-year median OS was 69.4%. Conclusion Administration of modified FLOT regimen in locoregionally advanced gastric cancers is feasible in clinical practice with high completion rates, though requiring dose modifications due to the incidence of clinically relevant grade 3 to 5 toxicities. Early outcomes with the regimen are on par with survivals from the FLOT-AIO study